J. Semicond. > 2023, Volume 44 > Issue 2 > 023104

REVIEWS

Advanced biosensing technologies for monitoring of agriculture pests and diseases: A review

Jiayao He, Ke Chen, Xubin Pan, Junfeng Zhai and Xiangmei Lin

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 Corresponding author: Junfeng Zhai, zjf1208@163.com; Xiangmei Lin, linxm@caiq.org.cn

DOI: 10.1088/1674-4926/44/2/023104

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Abstract: The threat posed to crop production by pests and diseases is one of the key factors that could reduce global food security. Early detection is of critical importance to make accurate predictions, optimize control strategies and prevent crop losses. Recent technological advancements highlight the opportunity to revolutionize monitoring of pests and diseases. Biosensing methodologies offer potential solutions for real-time and automated monitoring, which allow advancements in early and accurate detection and thus support sustainable crop protection. Herein, advanced biosensing technologies for pests and diseases monitoring, including image-based technologies, electronic noses, and wearable sensing methods are presented. Besides, challenges and future perspectives for widespread adoption of these technologies are discussed. Moreover, we believe it is necessary to integrate technologies through interdisciplinary cooperation for further exploration, which may provide unlimited possibilities for innovations and applications of agriculture monitoring.

Key words: precision agriculturebiosensorscropsdisease and pest management

Agriculture is crucial to economic growth particularly in developing countries[1]. A 2022 report from the Food and Agriculture Organization of the United Nations (FAO) found that between 702 and 828 million people in the world faced hunger, and furthermore around 2.31 billion people, nearly one in three people in the world, were moderately or severely food insecure. Crop yields being negatively impacted by the occurrences of pests and diseases put extra pressure on increasing demands for agricultural productivity. In fact, plant pests and diseases that interfere with crop growth cause substantial losses, and destroy our food supply and agricultural industries[2, 3]. To mitigate the impacts, the monitoring of pests and diseases has been essential for taking proper prevention steps and optimizing control strategies. Besides, monitoring data is crucial to build prediction models to forecast potential outbreaks, and can accordingly guide decision-making[4]. Thus, the monitoring of plant pests and diseases in the early stages plays important roles to prevent crop loss and ensure food security.

Conventional monitoring of crop pests and diseases depends on visual inspection through field observation, with demands for skilled people and on-site visits, resulting in the labor intensive and low cost-effective systems and showing difficulties to detect pests and pathogens at the early onset of the symptom. Moreover, the measurements may not allow analyses on the spatial-temporal variability of pest damage and disease developments due to poor resolution and low efficiency. Molecular identification methods, such as polymerase chain reaction (PCR), are destructive in the sampling and diagnosis procedures, with the requirements of sample preparation, trained operators and expensive reagents. As agriculture is in the middle of the digital revolution[5], recent developments in agricultural technology have brought increasing demands for automated and non-destructive monitoring methods, which are expected to detect the pests and diseases at the early stage[6].

In the context of precision agriculture (PA), it is crucial to optimize resource usage for enhancing agricultural yields and minimizing hazards to environmental and human health[7-9]. Integration of innovative technologies can provide agriculture monitoring with accurate, high-resolution, multidimensional data to afford reliable prediction and early detection of pests and pathogens, eventually achieving sustainable precision crop protection. In detail, reliable data on the stress level of crops, presence of organisms, and identification of pests and diseases are needed to plan targeted plant protection strategies, thus preventing the spread of pests and diseases in a time manner. It is likely to obtain initial preliminary diagnoses through sensing technology for its capacity to detect plant stresses[10]. The array-based biosensors enable the transduction of biotic and abiotic variables such as plant physiology, metabolites, or microclimate into electrical signals for real-time monitoring. It is worth noted that sensing information needs to be linked directly to biological traits for more extensive application in the monitoring of crop pests and diseases[11, 12]. Furthermore, high-resolution data from biosensors would be used to develop prediction models and allow fine-scale insights of dynamics regarding morphology, behavior and physiology, providing baseline data for surveillance and implementation of control measures. In particular, pests damage and diseases developments are highly relevant to plant traits and environmental factors[13]. For instance, crop-environmental parameters, in combination with pests and pathogens dynamics, can be further processed to examine features of crucial events in the life cycle of those damaging organisms for occurrences and outbreaks forecasting[14]. It is crucial to investigate a multitude of biosensors for agriculture monitoring to enable pests or pathogens being detected early, in order to guide site-specific management and eradication programs.

Advanced biosensing technologies that could non-invasively, timely and automatically collect multidimensional data demonstrate a great potential to support biosecurity and agriculture surveillance[15]. Here we present an overview of state-of-the-art biosensing methods that can detect pests or diseases of agriculture crops, illustrating sensing mechanisms and representative applications, and also discuss future perspectives for agriculture monitoring, and the detection and identification of pests and pathogens in the crop production.

Since traditional survey methods have been labor-intensive, monitoring activities would face to a series of tradeoffs, such as reducing the number of locations and the frequency of sampling, leading to limited monitoring resolutions both spatially and temporally. Emerging technologies like automated image recognition methods offer potential solutions to revolutionize monitoring for more extensive coverage, thus provide more insights into population dynamics, distributions and interactions of insect pests and diseases[16]. Imaging-based techniques have been studied to detect and monitor plant stresses including pest infestation and disease development for decades. Advances in deep learning algorithms allow improvements of the automatic detection of pests and diseases on the basis of computer vision in order to reach higher efficiency and accuracy. A variety of symptomatic and asymptomatic changes would occur on plants in responses to pests and diseases. It is of paramount importance to early detect subtle or even invisible signs to minimize the negative effects. Various active and passive electro-optical sensors enable the detection of early changes in plant physiology. Imaging-based technologies, such as RGB/visible imaging, multi-spectral and hyperspectral sensors, thermal and fluorescence sensors, have provided a non-invasive and automated monitoring systems for detection and identification of insect pests and plant diseases[17, 18].

Healthy plants provide a higher reflection in the near infrared region and reflect less in the visible region, while infected plants increase their reflectance in the visible range and decrease slowly in the near infrared range. The biotic stress typically results in the variation of the reflectance that is possibly caused by a chlorophyll level reduction and transformations in internal structure[19]. Such changes may also occur before observable signs, leading to a timely measure. Infected plants show morphological and internal modifications that is highly specific, which enables detection and identification based on the spectral signature of plants. Notably, hyperspectral cameras can measure dozens to hundreds of narrow spectral bands across the electromagnetic spectrum to obtain images comprising of two spatial and one wavelength dimensions, therefore provide rich spectral information for detection, classification and quantification of diseases at an early age[20]. Marín-Ortiz et al.[21] identified the spectral variation (relevant spectral wavelengths) in tomato plants infected with Fusarium oxysporum, enabling detection during the incubation period in which the symptoms are not visible. Reflectance spectroscopy combined with linear discriminant models were able to discriminate infected plants from healthy ones with high accuracy (85%–93%), according to the changes in the reflectance of diseased leaves in the infrared range measured. Additionally, hyperspectral imaging was performed to detect maize plants that subjected to insect infestations, basing on the detectable changes in certain spectral bands of leaf reflectance profiles[22].

The utilization of smart sensor traps to monitor insect pests has been well-applied, such as acoustic detection for stored pests[23], or counting insect entry as the light is interrupted[24]. Nevertheless, imaging the trapped insects, often in combination with sticky trap or pheromone trap, is dominating in the development of automatic pest monitoring devices. Camera-based sensors use digital cameras for remote visual inspection of trapped insects for the real-time monitoring[25], or integrated with artificial intelligence techniques for automatic detection and counting of insect pests[26]. Pest detection systems capture the morphology, color, and texture through image processing. Such methods need a large number of annotated images that classified by expert entomologists to enhance image-identification algorithms, the accuracy needs further testing in the monitoring programs. Using camera to monitor insect pests, the image quality (e.g., resolution, illumination) should be ensured to assess insect presence and classification, particularly according to insect size and morphometric characteristics[4]. Besides, taking into accounts the energy supply and costs is necessary for the deployment of monitoring systems in the field. Shaked et al.[25] developed the semi-automatic trap with a 5-Mpixel camera (Omni Vision OV5647 NOIR Rasp Pi) to capture real-time images of the sticky plate surfaces that placed in the orchards, the entomologist classified the fruit flies based on the images with high-rate accuracy. Camera-based insect monitoring, paired with wireless sensor networks (WSNs), can greatly improve the probability of detection in the early stage of an incursion, and additionally allow predictions of outbreaks risk as well as report on the level of the pest population. In addition to image sensors, the optical sensors have proven to be effective in detecting insects via recording the wingbeat frequency based on disruption of the electric signal patterns caused by the partial occlusion of light from the wings, further facilitating insect identification through biometrics[27]. Potamitis et al.[28] presented a novel bimodal optoelectronic sensor based on Fresnel lenses and a stereo recorder, identifying the incoming insects from the optoacoustic spectrum profile of the wingbeat (Fig. 1).

Fig. 1.  (Color online) The bimodal sensor to detect insect wingbeat[28].

It is desirable to capture physiological changes guided by interactions of pests and their hosts for early detection, because the variations may occur at very early stage of diseases. Volatile organic compounds (VOCs) are released from certain parts of plants into the atmosphere or the soil, mediating the communications between the plants and the surroundings. Plants produce a wide array of VOCs, that are one of the immediate responses of plants to pest infestation or pathogen infection, acting as a defense against pests by means of various mechanisms, such as reducing pest attacks or attracting predators[29]. The electronic nose (e-nose), mainly composed by an array of sensors, has been used to detect and discriminate volatile compounds for diseases detection. The rationale for e-nose is to detect the variation of VOC compositions when crops have been attacked. E-noses have already been applied for early detection of stored grain insects[30, 31] or storage diseases[32], fungus[33], bacterial diseases[34] and viruses[35], as well as to distinguish different disease levels[36], showing promising discrimination to monitor rapidly, noninvasively, and cost-effectively. The chemical emissions are released to the surrounding gas phase from host plants, from which one can detect damage information from the pest-induced specific volatiles (i.e., volatile fingerprints) (Fig. 2). The sensor responses are consistent with the different VOC compositions which might be compared to the overall variation of a pool of reference gas samples[37].

Metal oxide semiconductor (MOS) gas sensors have been widely used in e-noses to distinguish VOCs due to their low manufacturing price and large response range[38]. For detecting target VOCs, metal oxide materials such as ZnO, SnO2, and TiO2 have been employed as a sensing layer. Wen et al.[39] developed a sweeping e-nose system for detection of citrus fruits infestation at early stages, containing a detection unit that is composed of MOS sensors and measurement & pattern recognition modules (Fig. 3). Thus, VOCs have been identified for differentiating citrus fruits infested with Bactrocera dorsalis, basing on measurements of the change of semiconductors conductivity in the presence of redox reactions on the sensitive material surface. Lampson et al.[40] developed a portable device to draw volatiles from pests or pest-damaged products over carbon black–polymer composite sensors and measure the change in resistance for each sensor. Furthermore, Biondi et al.[41] used a commercial e-nose equipped with a metal oxide sensor array to detect diseased potatoes infected with Ralstonia solanacearum and Clavibacter michiganensis subsp. sepedonicus, respectively achieving 81.3% and 57.4% classification of the samples in dynamic and static sampling procedures (active and passive gas-sampling). Additionally, the passive sampling allows effective discrimination in simulated conditions of bulk storage. Higher flow rates and appropriate sorbent materials may improve VOCs concentration effects to certain degrees for better discrimination[37].

Fig. 3.  (Color online) The principle of the e-nose detection for citrus fruits infested by B. dorsalis[39].

The nanomaterials-based biosensors have been widely-studied for sensing applications, particularly in electrochemical biosensing[42-44]. Carbon nanotubes (CNTs) are expected to have a mix of good electrical properties and VOC sensing ability, as they have exceptional physical and chemical properties[45]. The behavior of carbon nanostructure polymer composite sensor devices is based on the level of response, which is dependent on both the nature and concentration of the adsorbed molecular species. Greenshields et al.[46] report the chemical sensors based on carbon nanostructure-poly(vinyl alcohol) (PVA) composites to detect four fungi species in melons, revealing the sensors response to a stimulus (interaction with carbon nanostructure polymer composites) provided by the volatiles exhaled by fruits. The research team further developed a set of resistive sensors based on carbon nanostructures for fast detection and identification of two general of fungi (Rhizopus sp. and Aspergillus sp. section Nigri) on the strawberry fruits[47]. Moreover, Zhao et al.[48] functionalized the CNTs with carboxylic group to enhance the sensing properties, resulting that this sensor array was able to detect various VOCs with relatively high sensitivity. Furthermore, Verma et al.[49] demonstrated that “chemical nose” biosensors with gold nanoparticles can detect polymicrobial mixtures and identify bacteria with high accuracy. Besides, a promising approach has been proposed to exhibit high sensitivity for representative VOCs by conjugating a thiolated ligands on the molybdenum disulfide (MoS2) surface. Such surface modification may develop new ways to improve detection properties in the real-time VOC monitoring system, leading to more valuable applications for biological sensing[50]. Despite present biosensors are mostly proposed in environment safety and medical healthcare area, the fabrication of nanostructured biosensor has a potential for further applications on in-situ detection of agricultural monitoring.

In addition to detecting plant VOCs, the technology that capture pests volatile pheromone in sensitive manners would be attractive for detection before onset of pest infestation. Moitra et al.[51] developed the β-cyclodextrinylated MEMS devices for selective and sensitive detection of female sex pheromone of olive fruit pest, Bactocera oleae, and the detection limit of the devices has been achieved to a value as low as 0.297 ppq.

Fig. 2.  (Color online) (a) A schematic view of the electronic nose system. (b) Cylindrical sensor chamber with 6 metal–oxide–semiconductor sensors. (c) System procedure diagram with pump flow work that will control the sensor response in a specific cycle[31].

Despite the rapid development of wearable biosensors that make use of stretchy and flexile electronics, their application potential to monitor plant health has not been fully developed[52]. Plant surfaces show far more diverse microscale characteristics than the human epidermis, which play a vital role in the exchange of substances and energy between plants and nature[53]. Recent progresses on flexible electronic technology in the field of agriculture enable monitoring on leaves or stems of plants to measure subtle changes[54, 55]. Wearable biosensors, developed to assess the status of plant health, have been exploited to measure the physiological and biochemical variations continuously by profiling relevant trait and microclimate parameters. The nanomaterials-based biosensors feature high flexibility and excellent mechanical strength, improving accessibility of miniaturized and portable devices in the fields of wearable bioelectronic devices, which might meet the demands for on-site monitoring and early detection[43, 56]. For example, flexible, stretchable and wearable carbon nanotube/graphite sensors were worn on the fruits of Solanum melongena and Cucurbita pepo, connecting with a readout circuit to make the real-time measurement of plant growth.

Developing chemical sensors that can monitor plant sap flow enables comprehensive monitoring of plant health and provides possibilities for early detection of abiotic and biotic stresses. Recently, a flexible electronic sensor (Figs. 4(a) and 4(b)) was reported, that can harmlessly cohabitate with the plant and continuously monitor sap flow of plants in the complex environment (e.g., agricultural farm field or greenhouse)[53]. The measurements of sap flow rates provide a real-time tracking method to study plant behaviors by analyzing plant health, water consumption, and nutrient distribution. The flexible and shape-morphing sensor systems were proposed for the nondestructive and long-term integration with plant organs[57]. For instance, the leaf temperature sensor is based on the porous substrate to continuously monitor microenvironment temperature without causing any physical damage and showing excellent biocompatibility. The flexible humidity sensors are able to assess the plant transpiration processes that exchange water molecules with the ambient basing on the opening and closing of stomata[58]. Lan et al.[59] used graphene oxide (GO) as the humidity-sensitive material to produce a flexible capacitive-type GO-based humidity sensor attaching to the lower surface of a leaf (Fig. 4(c)). Besides, a tiny graphene sensor that taped to plants was developed to monitor plant transpiration process and water status[60]. It is worth noted that flexible devices fabricated with graphene and its derivatives have shown great potential for non-destructive monitoring of plant health.

Fig. 4.  (Color online) (a) Optical images of the flexible wearable sensor mounted on a single plant leaf. (b) Exploded view illustration of the sap flow sensor[53]. (c) Schematic illustration of the fabrication process of the flexible humidity sensor[59]. (d) Schematic of the integrated device attached onto the lower epidermis of the leaf to monitor transpiration processes. (e) Photo of the front view of the multimodal flexible plant healthcare device integrated with a room humidity sensor, leaf-surface humidity sensor, optical sensor, and temperature sensor[62].

Wearable plant sensors hold great promise for plant stresses detection, which could monitor variations on tissue temperature, transpiration rate, or aperture of stoma. However, plants’ responses to pests and diseases are far more complicated and hard to diagnose specifically through those limited physiological activities. Therefore, multimodal sensing systems with high sensitivity and selectivity need to be further developed in a complicated way[11, 61]. Lu et al.[62] proposed an integrated multimodal flexible sensor system using ZnIn2S4 (ZIS) nanosheets to monitor the plant status by investigate the factors pertaining to plant health (Fig. 4(d)). The system consists of a temperature sensor, a humidity sensor for ambient moisture measurement, a leaf-surface humidity sensor for plant monitoring, and an optical sensor (Fig. 4(e)). In particular, the humidity sensors are applied to monitor the ambient humidity and plant transpiration. Khan et al.[63] reported an ultralightweight flexible sensory platform based on bare die complementary metal oxide semiconductor (CMOS) chips, holding a light, temperature, and humidity sensor on a flexible polymer substrate. The platform that placed on the plant leaf is applied to monitor microclimate conditions surrounding a plant for accurate growth monitoring. Using flexible and biocompatible materials coupled with a smart compact design, Nassar et al.[64] developed compliant plant wearables integrating temperature, humidity and strain sensors, which can be intimately deployed on the soft surface of any plant to remotely and continuously evaluate optimal growth settings. A flexible electronic device, based on the integration of single-walled carbon nanotube (SWCNT) channels and graphitic electrodes, was mounted on the leaf surface for sensing organic vapor in air[65]. In addition, a wearable platform that could measure gas emissions was developed for real-time monitoring and early detection of plant diseases. Li et al.[66] reported a leaf-attachable chemiresistive sensor array for noninvasive diagnosis of late blight caused by Phytophthora infestans. The sensor was designed by integrating a graphene-based sensing material and flexible silver nanowire (AgNW) electrode on a stretchable kirigami-based substrate. The sensing system enables early detection (within 4 days of inoculation) of P. infestans infection with high sensitivity, and monitoring of abiotic stresses such as mechanical injuries. Moreover, recent studies have proposed that magnesium-ion batteries (MIB) could be used as a flexible integrated unit to power future self-powered systems[67]. As a promising technology, wearable electronics are capable of continuous monitoring to track the status of plant health in real-time, and are expected to apply for monitoring and detection of crop pests and diseases.

Overall, each of the reviewed technologies shows distinct advantages over traditional monitor methods toward continuous monitoring and automation, not only providing a path to detect pests or diseases at a much earlier stage, but also generating precise, high-resolution and multidimensional data. Monitoring techniques based on morphology, bio-markers regarding plant physiology and biochemistry (e.g., VOCs, metabolite), and the microclimate relevant to bioprocesses allow early detection of pests and diseases. The advanced sensing technologies provide unprecedented opportunities for more specific analyses to generate novel insights into interactions among individual organisms and environments, and thus offer a sustainable solution for effective monitoring for biosecurity.

Integration among technologies must be involved to improve plant health management and enhance crop production efficiency. It is indispensable to require interdisciplinary collaborations among plant science, electronic engineering and data science, which are likely to be future trends. Such integration is essential for efficient cocreation and advanced solutions to ensure the practical application being fit-for-purpose. These technologies include semiconductor engineering, wireless sensor network, data processing and management, as well as biology. The cooperation of agriculture experts would investigate the biometric signature of particular species, further to detect and identify pests and to diagnose the causal agents of diseases. Biosensor-based monitoring generate a large set of multidimensional data that can be transformed to biological information. For example, image-based monitoring approaches along with deep learning algorithms are developed for trapped insect detection to recognize and count the number of individuals[68]. Moreover, data collection can be automated with sensor technologies and robotics, widening possibilities for autonomous monitoring and providing indications at scales across the microscopic to landscape levels[69]. Unmanned aerial vehicles (UAVs) have been increasingly adopted for crop monitoring and disease detection due to the accessibility of high-solution image sensing and easy-to-use for in-field operation[70]. An approach to combine UAVs with sticky traps was proposed for automated detection of Drosophila suzukii, highlighting the detection potential of UAV imagery to monitor insects[71]. Besides, ground-based platforms for automated monitoring offer greater flexibility that provide fine-scaled data and minimize the disturbances to crops as well (legged robots) [72-74]. The potential of integrated technologies requires further exploration to achieve overall aims, and thus could provide unprecedented opportunities for applications of agriculture monitoring.

The advancements of the biosensing methodology have opened up new avenues for monitoring of pests and diseases, however, practical applications for long-term monitoring, accurate detection, and identification are still in the developmental phases. One of the challenges is processing multidimensional data into reliable measurements, which can be further translated into biological information for status assessment and decision making. As the alterations in biological characteristics captured in field may occur under biotic and abiotic stress combinations (e.g., mixed infections), to specifically discern the effects caused due to individual stressors remains a challenging task. It is necessary to build biosensor systems with high sensitivity and selectivity to meet demands for in-field monitoring. At the same time, the stability should be considered due to the variability of environmental factors. The high investment costs impede steps toward the continuous monitoring and automation to some extent, however, advancements in power sources and scheduling of operations will enable the long-term field deployment in a relatively cost- and power-effective way[75, 76]. Besides, novel hardware and software approaches for data storage and wireless data transmission are being explored, further upscaling agriculture monitoring and allowing real-time data capture[77]. Technological innovations are creating opportunities to allow effective monitoring and detection for precise agriculture and biosecurity. It will become a trend to use advanced biosensors strategically to meet specific needs for accurate, real-time and automated monitoring.

The work is supported by National Key Research and Development Program of China (Grant No. 2022YFC2602100) and Chinese Academy of Inspection and Quarantine (2022JK38).



[1]
Unnevehr L J. Causes of and constraints to agricultural and economic development: Discussion. Am J Agric Econ, 2007, 89, 1168 doi: 10.1111/j.1467-8276.2007.01078.x
[2]
Savary S, Willocquet L, Pethybridge S J, et al. The global burden of pathogens and pests on major food crops. Nat Ecol Evol, 2019, 3, 430 doi: 10.1038/s41559-018-0793-y
[3]
Strange R N, Scott P R. Plant disease: A threat to global food security. Annu Rev Phytopathol, 2005, 43, 83 doi: 10.1146/annurev.phyto.43.113004.133839
[4]
Preti M, Verheggen F, Angeli S. Insect pest monitoring with camera-equipped traps: Strengths and limitations. J Pest Sci, 2021, 94, 203 doi: 10.1007/s10340-020-01309-4
[5]
Weersink A, Fraser E, Pannell D, et al. Opportunities and challenges for big data in agricultural and environmental analysis. Annu Rev Resour Econ, 2018, 10, 19 doi: 10.1146/annurev-resource-100516-053654
[6]
Silva G, Tomlinson J, Onkokesung N, et al. Plant pest surveillance: From satellites to molecules. Emerg Top Life Sci, 2021, 5, 275 doi: 10.1042/ETLS20200300
[7]
Walter A, Finger R, Huber R, et al. Opinion: Smart farming is key to developing sustainable agriculture. Proc Natl Acad Sci USA, 2017, 114, 6148 doi: 10.1073/pnas.1707462114
[8]
Garnett T, Appleby M C, Balmford A, et al. Agriculture. Sustainable intensification in agriculture: Premises and policies. Science, 2013, 341, 33 doi: 10.1126/science.1234485
[9]
Stafford J V. Implementing precision agriculture in the 21st century. J Agric Eng Res, 2000, 76, 267 doi: 10.1006/jaer.2000.0577
[10]
Kashyap B, Kumar R. Sensing methodologies in agriculture for monitoring biotic stress in plants due to pathogens and pests. Inventions, 2021, 6, 29 doi: 10.3390/inventions6020029
[11]
Lee G, Wei Q S, Zhu Y. Emerging wearable sensors for plant health monitoring. Adv Funct Mater, 2021, 31, 2106475 doi: 10.1002/adfm.202106475
[12]
He D C, Zhan J S, Xie L H. Problems, challenges and future of plant disease management: From an ecological point of view. J Integr Agric, 2016, 15, 705 doi: 10.1016/S2095-3119(15)61300-4
[13]
Rossi V, Giosuè S, Caffi T. Modelling plant diseases for decision making in crop protection. Precision Crop Protection - the Challenge and Use of Heterogeneity. Dordrecht: Springer Netherlands, 2010, 241
[14]
Grünig M, Razavi E, Calanca P, et al. Applying deep neural networks to predict incidence and phenology of plant pests and diseases. Ecosphere, 2021, 12, e03791 doi: 10.1002/ecs2.3791
[15]
Cordier T, Forster D, Dufresne Y, et al. Supervised machine learning outperforms taxonomy-based environmental DNA metabarcoding applied to biomonitoring. Mol Ecol Resour, 2018, 18, 1381 doi: 10.1111/1755-0998.12926
[16]
van Klink R, August T, Bas Y, et al. Emerging technologies revolutionise insect ecology and monitoring. Trends Ecol Evol, 2022, 37, 872 doi: 10.1016/j.tree.2022.06.001
[17]
Mahlein A K. Plant disease detection by imaging sensors - parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis, 2016, 100, 241 doi: 10.1094/PDIS-03-15-0340-FE
[18]
Zhang J C, Huang Y B, Pu R L, et al. Monitoring plant diseases and pests through remote sensing technology: A review. Comput Electron Agric, 2019, 165, 104943 doi: 10.1016/j.compag.2019.104943
[19]
Ennouri K, Kallel A. Remote sensing: An advanced technique for crop condition assessment. Math Probl Eng, 2019, 2019, 1 doi: 10.1155/2019/9404565
[20]
Mahlein A K, Kuska M T, Thomas S, et al. Plant disease detection by hyperspectral imaging: From the lab to the field. Adv Animal Biosci, 2017, 8, 238 doi: 10.1017/S2040470017001248
[21]
Carlos M O J, María H C L, Veronica B F. Detection of significant wavelengths for identifying and classifying Fusarium oxysporum during the incubation period and water stress in Solanum lycopersicum plants using reflectance spectroscopy. J Plant Prot Res, 2019, 59, 244 doi: 10.24425/jppr.2019.129290
[22]
do Prado Ribeiro L, Klock A L S, Filho J A W, et al. Hyperspectral imaging to characterize plant-plant communication in response to insect herbivory. Plant Methods, 2018, 14, 54 doi: 10.1186/s13007-018-0322-7
[23]
Eliopoulos P A, Potamitis I, Kontodimas D Ch, et al. Detection of adult beetles inside the stored wheat mass based on their acoustic emissions. J Econ Entomol, 2015, 108, 2808 doi: 10.1093/jee/tov231
[24]
Holguin G A, Lehman B L, Hull L A, et al. Electronic traps for automated monitoring of insect populations. IFAC Proc Vol, 2010, 43, 49 doi: 10.3182/20101206-3-JP-3009.00008
[25]
Shaked B, Amore A, Ioannou C, et al. Electronic traps for detection and population monitoring of adult fruit flies (Diptera: Tephritidae). J Appl Entomol, 2018, 142, 43 doi: 10.1111/jen.12422
[26]
Ding W G, Taylor G. Automatic moth detection from trap images for pest management. Comput Electron Agric, 2016, 123, 17 doi: 10.1016/j.compag.2016.02.003
[27]
Welsh T J, Bentall D, Kwon C, et al. Automated surveillance of lepidopteran pests with smart optoelectronic sensor traps. Sustainability, 2022, 14, 9577 doi: 10.3390/su14159577
[28]
Potamitis I, Rigakis I, Vidakis N, et al. Affordable bimodal optical sensors to spread the use of automated insect monitoring. J Sens, 2018, 2018, 1 doi: 10.1155/2018/3949415
[29]
Maffei M E. Sites of synthesis, biochemistry and functional role of plant volatiles. S Afr N J Bot, 2010, 76, 612 doi: 10.1016/j.sajb.2010.03.003
[30]
Xu S, Zhou Z Y, Li K L, et al. Recognition of the duration and prediction of insect prevalence of stored rough rice infested by the red flour beetle (tribolium castaneum herbst) using an electronic nose. Sensors, 2017, 17, 688 doi: 10.3390/s17040688
[31]
Nouri B, Fotouhi K, Mohtasebi S S, et al. Detection of different densities of Ephestia kuehniella pest on white flour at different larvae instar by an electronic nose system. J Stored Prod Res, 2019, 84, 101522 doi: 10.1016/j.jspr.2019.101522
[32]
Rutolo M F, Iliescu D, Clarkson J P, et al. Early identification of potato storage disease using an array of metal-oxide based gas sensors. Postharvest Biol Technol, 2016, 116, 50 doi: 10.1016/j.postharvbio.2015.12.028
[33]
Labanska M, Jenkins S, Van Amsterdam S, et al. Detection of the fungal infection in post-harvest Onions by an electronic nose. 2022 IEEE International Symposium on Olfaction and Electronic Nose, 2022, 1 doi: 10.1109/ISOEN54820.2022.9789676
[34]
Chang K P P, Zakaria A, Abdul Nasir A S, et al. Analysis and feasibility study of plant disease using e-nose. 2014 IEEE International Conference on Control System, Computing and Engineering, 2015, 58 doi: 10.1109/ICCSCE.2014.7072689
[35]
Hazarika S, Choudhury R, Montazer B, et al. Detection of citrus tristeza virus in mandarin orange using a custom-developed electronic nose system. IEEE Trans Instrum Meas, 2020, 69, 9010 doi: 10.1109/TIM.2020.2997064
[36]
Srivastava S, Mishra G, Mishra H N. Fuzzy controller based E-nose classification of Sitophilus oryzae infestation in stored rice grain. Food Chem, 2019, 283, 604 doi: 10.1016/j.foodchem.2019.01.076
[37]
Cellini A, Blasioli S, Biondi E, et al. Potential applications and limitations of electronic nose devices for plant disease diagnosis. Sensors, 2017, 17, 2596 doi: 10.3390/s17112596
[38]
Zheng Z C, Zhang C. Electronic noses based on metal oxide semiconductor sensors for detecting crop diseases and insect pests. Comput Electron Agric, 2022, 197, 106988 doi: 10.1016/j.compag.2022.106988
[39]
Wen T, Zheng L Z, Dong S, et al. Rapid detection and classification of citrus fruits infestation by Bactrocera dorsalis (Hendel) based on electronic nose. Postharvest Biol Technol, 2019, 147, 156 doi: 10.1016/j.postharvbio.2018.09.017
[40]
Lampson B D, Han Y J, Khalilian A, et al. Development of a portable electronic nose for detection of pests and plant damage. Comput Electron Agric, 2014, 108, 87 doi: 10.1016/j.compag.2014.07.002
[41]
Biondi E, Blasioli S, Galeone A, et al. Detection of potato brown rot and ring rot by electronic nose: From laboratory to real scale. Talanta, 2014, 129, 422 doi: 10.1016/j.talanta.2014.04.057
[42]
Schroeder V, Savagatrup S, He M, et al. Carbon nanotube chemical sensors. Chem Rev, 2019, 119, 599 doi: 10.1021/acs.chemrev.8b00340
[43]
Cardoso R M, Pereira T S, Facure M H M, et al. Current progress in plant pathogen detection enabled by nanomaterials-based (bio)sensors. Sens Actuat Rep, 2022, 4, 100068 doi: 10.1016/j.snr.2021.100068
[44]
Rabti A, Raouafi N, Merkoçi A. Bio(Sensing) devices based on ferrocene-functionalized graphene and carbon nanotubes. Carbon, 2016, 108, 481 doi: 10.1016/j.carbon.2016.07.043
[45]
Onthath H, Maurya M R, Bykkam S, et al. Development and fabrication of carbon nanotube (CNT)/CuO nanocomposite for volatile organic compounds (VOCs) gas sensor application. Macromol Symp, 2022, 402, 2270202 doi: 10.1002/masy.202270202
[46]
Greenshields M W C C, Mamo M A, Coville N J, et al. Tristimulus mathematical treatment application for monitoring fungi infestation evolution in melon using the electrical response of carbon nanostructure-polymer composite based sensors. Sens Actuat B, 2013, 188, 378 doi: 10.1016/j.snb.2013.07.014
[47]
Greenshields M W C C, Cunha B B, Coville N J, et al. Fungi active microbial metabolism detection of rhizopus sp. and aspergillus sp. section nigri on strawberry using a set of chemical sensors based on carbon nanostructures. Chemosensors, 2016, 4, 19 doi: 10.3390/chemosensors4030019
[48]
Zhao D, Zhao B Y, Koltsov D, et al. Detection of VOCs and nitrogen containing gaseous molecules by utilizing carbon nanotubes (CNTs) as sensing materials. Meet Abstr, 2022, 2629 doi: 10.1149/MA2022-02632629mtgabs
[49]
Verma M S, Wei S C, Rogowski J L, et al. Interactions between bacterial surface and nanoparticles govern the performance of “chemical nose” biosensors. Biosens Bioelectron, 2016, 83, 115 doi: 10.1016/j.bios.2016.04.024
[50]
Kim J S, Yoo H W, Choi H O, et al. Tunable volatile organic compounds sensor by using thiolated ligand conjugation on MoS2. Nano Lett, 2014, 14, 5941 doi: 10.1021/nl502906a
[51]
Moitra P, Bhagat D, Kamble V B, et al. First example of engineered β-cyclodextrinylated MEMS devices for volatile pheromone sensing of olive fruit pests. Biosens Bioelectron, 2021, 173, 112728 doi: 10.1016/j.bios.2020.112728
[52]
Zheng Y Q, Wang Y W, Li Z X, et al. MXene quantum dots/perovskite heterostructure enabling highly specific ultraviolet detection for skin prevention. Matter, 2023, 6, 506 doi: 10.1016/j.matt.2022.11.020
[53]
Chai Y F, Chen C Y, Luo X, et al. Cohabiting plant-wearable sensor in situ monitors water transport in plant. Adv Sci, 2021, 8, 2003642 doi: 10.1002/advs.202003642
[54]
Giraldo J P, Wu H H, Newkirk G M, et al. Nanobiotechnology approaches for engineering smart plant sensors. Nat Nanotechnol, 2019, 14, 541 doi: 10.1038/s41565-019-0470-6
[55]
Liu J. Smart-agriculture: wearable devices for plant protection. In: Wearable Physical, Chemical and Biological Sensors. Amsterdam: Elsevier, 2022 doi: 10.1016/B978-0-12-821661-3.00002-1
[56]
Nezhad A S. Future of portable devices for plant pathogen diagnosis. Lab Chip, 2014, 14, 2887 doi: 10.1039/C4LC00487F
[57]
Dong K R, Wang Y C, Zhang R P, et al. Flexible and shape-morphing plant sensors designed for microenvironment temperature monitoring of irregular surfaces. Adv Mater Technol, 2022, 2201204 doi: 10.1002/admt.202201204
[58]
Lu Y Y, Yang G, Shen Y J, et al. Multifunctional flexible humidity sensor systems towards noncontact wearable electronics. Nanomicro Lett, 2022, 14, 150 doi: 10.1007/s40820-022-00895-5
[59]
Lan L Y, Le X H, Dong H Y, et al. One-step and large-scale fabrication of flexible and wearable humidity sensor based on laser-induced graphene for real-time tracking of plant transpiration at bio-interface. Biosens Bioelectron, 2020, 165, 112360 doi: 10.1016/j.bios.2020.112360
[60]
Oren S, Ceylan H, Schnable P S, et al. Wearable electronics: High-resolution patterning and transferring of graphene-based nanomaterials onto tape toward roll-to-roll production of tape-based wearable sensors. Adv Mater Technol, 2017, 2, 1770055 doi: 10.1002/admt.201770055
[61]
Li L L, Zhao S F, Ran W H, et al. Dual sensing signal decoupling based on tellurium anisotropy for VR interaction and neuro-reflex system application. Nat Commun, 2022, 13, 5975 doi: 10.1038/s41467-022-33716-9
[62]
Lu Y Y, Xu K C, Zhang L S, et al. Multimodal plant healthcare flexible sensor system. ACS Nano, 2020, 14, 10966 doi: 10.1021/acsnano.0c03757
[63]
Khan S M, Shaikh S F, Qaiser N, et al. Flexible lightweight CMOS-enabled multisensory platform for plant microclimate monitoring. IEEE Trans Electron Devices, 2018, 65, 5038 doi: 10.1109/TED.2018.2872401
[64]
Nassar J M, Khan S M, Villalva D R, et al. Compliant plant wearables for localized microclimate and plant growth monitoring. Npj Flex Electron, 2018, 2, 24 doi: 10.1038/s41528-018-0039-8
[65]
Lee K, Park J, Lee M S, et al. In-situ synthesis of carbon nanotube-graphite electronic devices and their integrations onto surfaces of live plants and insects. Nano Lett, 2014, 14, 2647 doi: 10.1021/nl500513n
[66]
Li Z, Liu Y X, Hossain O, et al. Real-time monitoring of plant stresses via chemiresistive profiling of leaf volatiles by a wearable sensor. Matter, 2021, 4, 2553 doi: 10.1016/j.matt.2021.06.009
[67]
Zhang Y M, Cao J M, Yuan Z Y, et al. TiVCTx MXene/chalcogenide heterostructure-based high-performance magnesium-ion battery as flexible integrated units. Small, 2022, 18, 2202313 doi: 10.1002/smll.202202313
[68]
Høye T T, Ärje J, Bjerge K, et al. Deep learning and computer vision will transform entomology. Proc Natl Acad Sci USA, 2021, 118, e2002545117 doi: 10.1073/pnas.2002545117
[69]
Galieni A, D'Ascenzo N, Stagnari F, et al. Past and future of plant stress detection: An overview from remote sensing to positron emission tomography. Front Plant Sci, 2021, 11, 609155 doi: 10.3389/fpls.2020.609155
[70]
Barbedo J. A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses. Drones, 2019, 3, 40 doi: 10.3390/drones3020040
[71]
Roosjen P P, Kellenberger B, Kooistra L, et al. Deep learning for automated detection of Drosophila suzukii: Potential for UAV-based monitoring. Pest Manag Sci, 2020, 76, 2994 doi: 10.1002/ps.5845
[72]
Shanmugapriya P, Rathika S, Ramesh T, et al. Applications of remote sensing in agriculture - A review. Int J Curr Microbiol App Sci, 2019, 8, 2270 doi: 10.20546/ijcmas.2019.801.238
[73]
Bietresato M, Carabin G, D'Auria D, et al. A tracked mobile robotic lab for monitoring the plants volume and health. 2016 12th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, 2016, 1 doi: 10.1109/MESA.2016.7587134
[74]
Hu Z H, Liu B Y, Zhao Y C. Agricultural robot for intelligent detection of Pyralidae insects. Agricultural Robots - Fundamentals and Applications. London: IntechOpen, 2019 doi: 10.5772/intechopen.79460
[75]
Zhao S F, Ran W H, Lou Z, et al. Neuromorphic-computing-based adaptive learning using ion dynamics in flexible energy storage devices. Natl Sci Rev, 2022, 9, nwac158 doi: 10.1093/nsr/nwac158
[76]
Potamitis I, Eliopoulos P, Rigakis I. Automated remote insect surveillance at a global scale and the Internet of Things. Robotics, 2017, 6, 19 doi: 10.3390/robotics6030019
[77]
Chettri L, Bera R. A comprehensive survey on Internet of Things (IoT) toward 5G wireless systems. IEEE Internet Things J, 2020, 7, 16 doi: 10.1109/JIOT.2019.2948888
Fig. 1.  (Color online) The bimodal sensor to detect insect wingbeat[28].

Fig. 3.  (Color online) The principle of the e-nose detection for citrus fruits infested by B. dorsalis[39].

Fig. 2.  (Color online) (a) A schematic view of the electronic nose system. (b) Cylindrical sensor chamber with 6 metal–oxide–semiconductor sensors. (c) System procedure diagram with pump flow work that will control the sensor response in a specific cycle[31].

Fig. 4.  (Color online) (a) Optical images of the flexible wearable sensor mounted on a single plant leaf. (b) Exploded view illustration of the sap flow sensor[53]. (c) Schematic illustration of the fabrication process of the flexible humidity sensor[59]. (d) Schematic of the integrated device attached onto the lower epidermis of the leaf to monitor transpiration processes. (e) Photo of the front view of the multimodal flexible plant healthcare device integrated with a room humidity sensor, leaf-surface humidity sensor, optical sensor, and temperature sensor[62].

[1]
Unnevehr L J. Causes of and constraints to agricultural and economic development: Discussion. Am J Agric Econ, 2007, 89, 1168 doi: 10.1111/j.1467-8276.2007.01078.x
[2]
Savary S, Willocquet L, Pethybridge S J, et al. The global burden of pathogens and pests on major food crops. Nat Ecol Evol, 2019, 3, 430 doi: 10.1038/s41559-018-0793-y
[3]
Strange R N, Scott P R. Plant disease: A threat to global food security. Annu Rev Phytopathol, 2005, 43, 83 doi: 10.1146/annurev.phyto.43.113004.133839
[4]
Preti M, Verheggen F, Angeli S. Insect pest monitoring with camera-equipped traps: Strengths and limitations. J Pest Sci, 2021, 94, 203 doi: 10.1007/s10340-020-01309-4
[5]
Weersink A, Fraser E, Pannell D, et al. Opportunities and challenges for big data in agricultural and environmental analysis. Annu Rev Resour Econ, 2018, 10, 19 doi: 10.1146/annurev-resource-100516-053654
[6]
Silva G, Tomlinson J, Onkokesung N, et al. Plant pest surveillance: From satellites to molecules. Emerg Top Life Sci, 2021, 5, 275 doi: 10.1042/ETLS20200300
[7]
Walter A, Finger R, Huber R, et al. Opinion: Smart farming is key to developing sustainable agriculture. Proc Natl Acad Sci USA, 2017, 114, 6148 doi: 10.1073/pnas.1707462114
[8]
Garnett T, Appleby M C, Balmford A, et al. Agriculture. Sustainable intensification in agriculture: Premises and policies. Science, 2013, 341, 33 doi: 10.1126/science.1234485
[9]
Stafford J V. Implementing precision agriculture in the 21st century. J Agric Eng Res, 2000, 76, 267 doi: 10.1006/jaer.2000.0577
[10]
Kashyap B, Kumar R. Sensing methodologies in agriculture for monitoring biotic stress in plants due to pathogens and pests. Inventions, 2021, 6, 29 doi: 10.3390/inventions6020029
[11]
Lee G, Wei Q S, Zhu Y. Emerging wearable sensors for plant health monitoring. Adv Funct Mater, 2021, 31, 2106475 doi: 10.1002/adfm.202106475
[12]
He D C, Zhan J S, Xie L H. Problems, challenges and future of plant disease management: From an ecological point of view. J Integr Agric, 2016, 15, 705 doi: 10.1016/S2095-3119(15)61300-4
[13]
Rossi V, Giosuè S, Caffi T. Modelling plant diseases for decision making in crop protection. Precision Crop Protection - the Challenge and Use of Heterogeneity. Dordrecht: Springer Netherlands, 2010, 241
[14]
Grünig M, Razavi E, Calanca P, et al. Applying deep neural networks to predict incidence and phenology of plant pests and diseases. Ecosphere, 2021, 12, e03791 doi: 10.1002/ecs2.3791
[15]
Cordier T, Forster D, Dufresne Y, et al. Supervised machine learning outperforms taxonomy-based environmental DNA metabarcoding applied to biomonitoring. Mol Ecol Resour, 2018, 18, 1381 doi: 10.1111/1755-0998.12926
[16]
van Klink R, August T, Bas Y, et al. Emerging technologies revolutionise insect ecology and monitoring. Trends Ecol Evol, 2022, 37, 872 doi: 10.1016/j.tree.2022.06.001
[17]
Mahlein A K. Plant disease detection by imaging sensors - parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis, 2016, 100, 241 doi: 10.1094/PDIS-03-15-0340-FE
[18]
Zhang J C, Huang Y B, Pu R L, et al. Monitoring plant diseases and pests through remote sensing technology: A review. Comput Electron Agric, 2019, 165, 104943 doi: 10.1016/j.compag.2019.104943
[19]
Ennouri K, Kallel A. Remote sensing: An advanced technique for crop condition assessment. Math Probl Eng, 2019, 2019, 1 doi: 10.1155/2019/9404565
[20]
Mahlein A K, Kuska M T, Thomas S, et al. Plant disease detection by hyperspectral imaging: From the lab to the field. Adv Animal Biosci, 2017, 8, 238 doi: 10.1017/S2040470017001248
[21]
Carlos M O J, María H C L, Veronica B F. Detection of significant wavelengths for identifying and classifying Fusarium oxysporum during the incubation period and water stress in Solanum lycopersicum plants using reflectance spectroscopy. J Plant Prot Res, 2019, 59, 244 doi: 10.24425/jppr.2019.129290
[22]
do Prado Ribeiro L, Klock A L S, Filho J A W, et al. Hyperspectral imaging to characterize plant-plant communication in response to insect herbivory. Plant Methods, 2018, 14, 54 doi: 10.1186/s13007-018-0322-7
[23]
Eliopoulos P A, Potamitis I, Kontodimas D Ch, et al. Detection of adult beetles inside the stored wheat mass based on their acoustic emissions. J Econ Entomol, 2015, 108, 2808 doi: 10.1093/jee/tov231
[24]
Holguin G A, Lehman B L, Hull L A, et al. Electronic traps for automated monitoring of insect populations. IFAC Proc Vol, 2010, 43, 49 doi: 10.3182/20101206-3-JP-3009.00008
[25]
Shaked B, Amore A, Ioannou C, et al. Electronic traps for detection and population monitoring of adult fruit flies (Diptera: Tephritidae). J Appl Entomol, 2018, 142, 43 doi: 10.1111/jen.12422
[26]
Ding W G, Taylor G. Automatic moth detection from trap images for pest management. Comput Electron Agric, 2016, 123, 17 doi: 10.1016/j.compag.2016.02.003
[27]
Welsh T J, Bentall D, Kwon C, et al. Automated surveillance of lepidopteran pests with smart optoelectronic sensor traps. Sustainability, 2022, 14, 9577 doi: 10.3390/su14159577
[28]
Potamitis I, Rigakis I, Vidakis N, et al. Affordable bimodal optical sensors to spread the use of automated insect monitoring. J Sens, 2018, 2018, 1 doi: 10.1155/2018/3949415
[29]
Maffei M E. Sites of synthesis, biochemistry and functional role of plant volatiles. S Afr N J Bot, 2010, 76, 612 doi: 10.1016/j.sajb.2010.03.003
[30]
Xu S, Zhou Z Y, Li K L, et al. Recognition of the duration and prediction of insect prevalence of stored rough rice infested by the red flour beetle (tribolium castaneum herbst) using an electronic nose. Sensors, 2017, 17, 688 doi: 10.3390/s17040688
[31]
Nouri B, Fotouhi K, Mohtasebi S S, et al. Detection of different densities of Ephestia kuehniella pest on white flour at different larvae instar by an electronic nose system. J Stored Prod Res, 2019, 84, 101522 doi: 10.1016/j.jspr.2019.101522
[32]
Rutolo M F, Iliescu D, Clarkson J P, et al. Early identification of potato storage disease using an array of metal-oxide based gas sensors. Postharvest Biol Technol, 2016, 116, 50 doi: 10.1016/j.postharvbio.2015.12.028
[33]
Labanska M, Jenkins S, Van Amsterdam S, et al. Detection of the fungal infection in post-harvest Onions by an electronic nose. 2022 IEEE International Symposium on Olfaction and Electronic Nose, 2022, 1 doi: 10.1109/ISOEN54820.2022.9789676
[34]
Chang K P P, Zakaria A, Abdul Nasir A S, et al. Analysis and feasibility study of plant disease using e-nose. 2014 IEEE International Conference on Control System, Computing and Engineering, 2015, 58 doi: 10.1109/ICCSCE.2014.7072689
[35]
Hazarika S, Choudhury R, Montazer B, et al. Detection of citrus tristeza virus in mandarin orange using a custom-developed electronic nose system. IEEE Trans Instrum Meas, 2020, 69, 9010 doi: 10.1109/TIM.2020.2997064
[36]
Srivastava S, Mishra G, Mishra H N. Fuzzy controller based E-nose classification of Sitophilus oryzae infestation in stored rice grain. Food Chem, 2019, 283, 604 doi: 10.1016/j.foodchem.2019.01.076
[37]
Cellini A, Blasioli S, Biondi E, et al. Potential applications and limitations of electronic nose devices for plant disease diagnosis. Sensors, 2017, 17, 2596 doi: 10.3390/s17112596
[38]
Zheng Z C, Zhang C. Electronic noses based on metal oxide semiconductor sensors for detecting crop diseases and insect pests. Comput Electron Agric, 2022, 197, 106988 doi: 10.1016/j.compag.2022.106988
[39]
Wen T, Zheng L Z, Dong S, et al. Rapid detection and classification of citrus fruits infestation by Bactrocera dorsalis (Hendel) based on electronic nose. Postharvest Biol Technol, 2019, 147, 156 doi: 10.1016/j.postharvbio.2018.09.017
[40]
Lampson B D, Han Y J, Khalilian A, et al. Development of a portable electronic nose for detection of pests and plant damage. Comput Electron Agric, 2014, 108, 87 doi: 10.1016/j.compag.2014.07.002
[41]
Biondi E, Blasioli S, Galeone A, et al. Detection of potato brown rot and ring rot by electronic nose: From laboratory to real scale. Talanta, 2014, 129, 422 doi: 10.1016/j.talanta.2014.04.057
[42]
Schroeder V, Savagatrup S, He M, et al. Carbon nanotube chemical sensors. Chem Rev, 2019, 119, 599 doi: 10.1021/acs.chemrev.8b00340
[43]
Cardoso R M, Pereira T S, Facure M H M, et al. Current progress in plant pathogen detection enabled by nanomaterials-based (bio)sensors. Sens Actuat Rep, 2022, 4, 100068 doi: 10.1016/j.snr.2021.100068
[44]
Rabti A, Raouafi N, Merkoçi A. Bio(Sensing) devices based on ferrocene-functionalized graphene and carbon nanotubes. Carbon, 2016, 108, 481 doi: 10.1016/j.carbon.2016.07.043
[45]
Onthath H, Maurya M R, Bykkam S, et al. Development and fabrication of carbon nanotube (CNT)/CuO nanocomposite for volatile organic compounds (VOCs) gas sensor application. Macromol Symp, 2022, 402, 2270202 doi: 10.1002/masy.202270202
[46]
Greenshields M W C C, Mamo M A, Coville N J, et al. Tristimulus mathematical treatment application for monitoring fungi infestation evolution in melon using the electrical response of carbon nanostructure-polymer composite based sensors. Sens Actuat B, 2013, 188, 378 doi: 10.1016/j.snb.2013.07.014
[47]
Greenshields M W C C, Cunha B B, Coville N J, et al. Fungi active microbial metabolism detection of rhizopus sp. and aspergillus sp. section nigri on strawberry using a set of chemical sensors based on carbon nanostructures. Chemosensors, 2016, 4, 19 doi: 10.3390/chemosensors4030019
[48]
Zhao D, Zhao B Y, Koltsov D, et al. Detection of VOCs and nitrogen containing gaseous molecules by utilizing carbon nanotubes (CNTs) as sensing materials. Meet Abstr, 2022, 2629 doi: 10.1149/MA2022-02632629mtgabs
[49]
Verma M S, Wei S C, Rogowski J L, et al. Interactions between bacterial surface and nanoparticles govern the performance of “chemical nose” biosensors. Biosens Bioelectron, 2016, 83, 115 doi: 10.1016/j.bios.2016.04.024
[50]
Kim J S, Yoo H W, Choi H O, et al. Tunable volatile organic compounds sensor by using thiolated ligand conjugation on MoS2. Nano Lett, 2014, 14, 5941 doi: 10.1021/nl502906a
[51]
Moitra P, Bhagat D, Kamble V B, et al. First example of engineered β-cyclodextrinylated MEMS devices for volatile pheromone sensing of olive fruit pests. Biosens Bioelectron, 2021, 173, 112728 doi: 10.1016/j.bios.2020.112728
[52]
Zheng Y Q, Wang Y W, Li Z X, et al. MXene quantum dots/perovskite heterostructure enabling highly specific ultraviolet detection for skin prevention. Matter, 2023, 6, 506 doi: 10.1016/j.matt.2022.11.020
[53]
Chai Y F, Chen C Y, Luo X, et al. Cohabiting plant-wearable sensor in situ monitors water transport in plant. Adv Sci, 2021, 8, 2003642 doi: 10.1002/advs.202003642
[54]
Giraldo J P, Wu H H, Newkirk G M, et al. Nanobiotechnology approaches for engineering smart plant sensors. Nat Nanotechnol, 2019, 14, 541 doi: 10.1038/s41565-019-0470-6
[55]
Liu J. Smart-agriculture: wearable devices for plant protection. In: Wearable Physical, Chemical and Biological Sensors. Amsterdam: Elsevier, 2022 doi: 10.1016/B978-0-12-821661-3.00002-1
[56]
Nezhad A S. Future of portable devices for plant pathogen diagnosis. Lab Chip, 2014, 14, 2887 doi: 10.1039/C4LC00487F
[57]
Dong K R, Wang Y C, Zhang R P, et al. Flexible and shape-morphing plant sensors designed for microenvironment temperature monitoring of irregular surfaces. Adv Mater Technol, 2022, 2201204 doi: 10.1002/admt.202201204
[58]
Lu Y Y, Yang G, Shen Y J, et al. Multifunctional flexible humidity sensor systems towards noncontact wearable electronics. Nanomicro Lett, 2022, 14, 150 doi: 10.1007/s40820-022-00895-5
[59]
Lan L Y, Le X H, Dong H Y, et al. One-step and large-scale fabrication of flexible and wearable humidity sensor based on laser-induced graphene for real-time tracking of plant transpiration at bio-interface. Biosens Bioelectron, 2020, 165, 112360 doi: 10.1016/j.bios.2020.112360
[60]
Oren S, Ceylan H, Schnable P S, et al. Wearable electronics: High-resolution patterning and transferring of graphene-based nanomaterials onto tape toward roll-to-roll production of tape-based wearable sensors. Adv Mater Technol, 2017, 2, 1770055 doi: 10.1002/admt.201770055
[61]
Li L L, Zhao S F, Ran W H, et al. Dual sensing signal decoupling based on tellurium anisotropy for VR interaction and neuro-reflex system application. Nat Commun, 2022, 13, 5975 doi: 10.1038/s41467-022-33716-9
[62]
Lu Y Y, Xu K C, Zhang L S, et al. Multimodal plant healthcare flexible sensor system. ACS Nano, 2020, 14, 10966 doi: 10.1021/acsnano.0c03757
[63]
Khan S M, Shaikh S F, Qaiser N, et al. Flexible lightweight CMOS-enabled multisensory platform for plant microclimate monitoring. IEEE Trans Electron Devices, 2018, 65, 5038 doi: 10.1109/TED.2018.2872401
[64]
Nassar J M, Khan S M, Villalva D R, et al. Compliant plant wearables for localized microclimate and plant growth monitoring. Npj Flex Electron, 2018, 2, 24 doi: 10.1038/s41528-018-0039-8
[65]
Lee K, Park J, Lee M S, et al. In-situ synthesis of carbon nanotube-graphite electronic devices and their integrations onto surfaces of live plants and insects. Nano Lett, 2014, 14, 2647 doi: 10.1021/nl500513n
[66]
Li Z, Liu Y X, Hossain O, et al. Real-time monitoring of plant stresses via chemiresistive profiling of leaf volatiles by a wearable sensor. Matter, 2021, 4, 2553 doi: 10.1016/j.matt.2021.06.009
[67]
Zhang Y M, Cao J M, Yuan Z Y, et al. TiVCTx MXene/chalcogenide heterostructure-based high-performance magnesium-ion battery as flexible integrated units. Small, 2022, 18, 2202313 doi: 10.1002/smll.202202313
[68]
Høye T T, Ärje J, Bjerge K, et al. Deep learning and computer vision will transform entomology. Proc Natl Acad Sci USA, 2021, 118, e2002545117 doi: 10.1073/pnas.2002545117
[69]
Galieni A, D'Ascenzo N, Stagnari F, et al. Past and future of plant stress detection: An overview from remote sensing to positron emission tomography. Front Plant Sci, 2021, 11, 609155 doi: 10.3389/fpls.2020.609155
[70]
Barbedo J. A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses. Drones, 2019, 3, 40 doi: 10.3390/drones3020040
[71]
Roosjen P P, Kellenberger B, Kooistra L, et al. Deep learning for automated detection of Drosophila suzukii: Potential for UAV-based monitoring. Pest Manag Sci, 2020, 76, 2994 doi: 10.1002/ps.5845
[72]
Shanmugapriya P, Rathika S, Ramesh T, et al. Applications of remote sensing in agriculture - A review. Int J Curr Microbiol App Sci, 2019, 8, 2270 doi: 10.20546/ijcmas.2019.801.238
[73]
Bietresato M, Carabin G, D'Auria D, et al. A tracked mobile robotic lab for monitoring the plants volume and health. 2016 12th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, 2016, 1 doi: 10.1109/MESA.2016.7587134
[74]
Hu Z H, Liu B Y, Zhao Y C. Agricultural robot for intelligent detection of Pyralidae insects. Agricultural Robots - Fundamentals and Applications. London: IntechOpen, 2019 doi: 10.5772/intechopen.79460
[75]
Zhao S F, Ran W H, Lou Z, et al. Neuromorphic-computing-based adaptive learning using ion dynamics in flexible energy storage devices. Natl Sci Rev, 2022, 9, nwac158 doi: 10.1093/nsr/nwac158
[76]
Potamitis I, Eliopoulos P, Rigakis I. Automated remote insect surveillance at a global scale and the Internet of Things. Robotics, 2017, 6, 19 doi: 10.3390/robotics6030019
[77]
Chettri L, Bera R. A comprehensive survey on Internet of Things (IoT) toward 5G wireless systems. IEEE Internet Things J, 2020, 7, 16 doi: 10.1109/JIOT.2019.2948888
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      Jiayao He, Ke Chen, Xubin Pan, Junfeng Zhai, Xiangmei Lin. Advanced biosensing technologies for monitoring of agriculture pests and diseases: A review[J]. Journal of Semiconductors, 2023, 44(2): 023104. doi: 10.1088/1674-4926/44/2/023104 ****J Y He, K Chen, X B Pan, J F Zhai, X M Lin. Advanced biosensing technologies for monitoring of agriculture pests and diseases: A review[J]. J. Semicond, 2023, 44(2): 023104. doi: 10.1088/1674-4926/44/2/023104
      Citation:
      Jiayao He, Ke Chen, Xubin Pan, Junfeng Zhai, Xiangmei Lin. Advanced biosensing technologies for monitoring of agriculture pests and diseases: A review[J]. Journal of Semiconductors, 2023, 44(2): 023104. doi: 10.1088/1674-4926/44/2/023104 ****
      J Y He, K Chen, X B Pan, J F Zhai, X M Lin. Advanced biosensing technologies for monitoring of agriculture pests and diseases: A review[J]. J. Semicond, 2023, 44(2): 023104. doi: 10.1088/1674-4926/44/2/023104

      Advanced biosensing technologies for monitoring of agriculture pests and diseases: A review

      DOI: 10.1088/1674-4926/44/2/023104
      More Information
      • Jiayao He:focuses on plant protection, pest risk analysis, and monitoring of agriculture pests and diseases
      • Junfeng Zhai:got his Doctor’s degree of agricultural biotechnology at Jilin Agricultural University in 2013. His research fields include biotechnology development, biological safety evaluation, and monitoring of agriculture pests and diseases
      • Xiangmei Lin:got her Doctor’s degree of Veterinary Pathology at Nanjing Agricultural University in 1998. Her research interests include detection and monitoring technologies for animal diseases, zoonotic diseases, foreign animal diseases and monitoring technologies for the detection of genetic modification in animals
      • Corresponding author: zjf1208@163.comlinxm@caiq.org.cn
      • Received Date: 2022-11-25
      • Revised Date: 2023-01-13
      • Available Online: 2023-02-03

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