From detection to action: artificial intelligence in integrated pest and invasive plant management

Abstract

Global agriculture faces growing threats from pests, pathogens, and invasive species, intensified by climate change and biodiversity losses. Conventional approaches are limited in both precision and scale, and artificial intelligence (AI) is now reshaping integrated pest management (IPM). Modern agricultural monitoring leverages high-resolution observations, hyperspectral sensors, and the Internet of Things (IoT) to facilitate early detection for diseases. Hybrid AI systems can integrate multi-source data to enhance the accuracy of real-time monitoring of pest and disease dynamics, including the detection and tracking of sparse invasive populations and their biomass even under shifting climate scenarios. They further enable predictive forecasting and the optimization of management strategies. When AI-driven diagnostics are integrated with autonomous robotics, they form a robust framework for epidemic mitigation. Here, we provide a systematic macro-perspective review, bridging foundational AI mechanisms with actionable IPM intelligence. We analyze the evolution of agricultural AI from cross-modal architectures to multi-scale diagnostics and full-lifecycle interventions. Finally, we propose a framework for the global agroecological network, offering a sustainable path toward maximizing productivity while ensuring ecological resilience.

Keywords

Precision Agriculture / Integrated Pest Management / Artificial Intelligence

References

[1] Abd. Kharim MN, Wayayok A, Mohamed Shariff AR, et al. . Droplet deposition density of organic liquid fertilizer at low altitude UAV aerial spraying in rice cultivation. Comput Electron Agric, 2019, 167: 105045

[2] Al-Najadi R, Al-Mulla Y, Al-Abri I, Al-Sadi AM. Effectiveness of drone-based thermal sensors in optimizing controlled environment agriculture performance under arid conditions. Sci Rep, 2025, 15: 9042

[3] Al-Saddik H, Laybros A, Billiot B, et al. . Using image texture and spectral reflectance analysis to detect yellowness and esca in grapevines at leaf-level. Remote Sens, 2018, 10: 618

[4] Arcot S, Masum M, Kader MS, et al. TinyML for cybersecurity: deploying optimized deep learning models for on-device threat detection on resource-constrained devices. In: 2024 IEEE International Conference on Big Data (BigData); 2024:5542–5550. https://doi.org/10.1109/bigdata62323.2024.10825955.

[5] Atef M, Khattab A, Agamy EA, Khairy MM. Deep learning based time-series forecasting framework for olive precision farming. In: 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS); 2021:1062–1065. https://doi.org/10.1109/mwscas47672.2021.9531929.

[6] Ay F, İnce G. Application of pesticide using unmanned aerial vehicle. In: 2015 23nd signal processing and communications applications conference (SIU); 2015:1268–1271. https://doi.org/10.1109/siu.2015.7130069.

[7] Bac CW, van Henten EJ, Hemming J, Edan Y. Harvesting robots for high-value crops: state-of-the-art review and challenges ahead. J Field Robot, 2014, 31: 888-911

[8] Barbedo JGA. A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst Eng, 2016, 144: 52-60

[9] Bhattacharya BK, Chattopadhyay C. A multi-stage tracking for mustard rot disease combining surface meteorology and satellite remote sensing. Comput Electron Agric, 2013, 90: 35-44

[10] Bi L, Hu G, Raza MM, et al. . A gated recurrent units (GRU)-based model for early detection of soybean sudden death syndrome through time-series satellite imagery. Remote Sens, 2020, 12: 3621

[11] Biglia A, Grella M, Bloise N, et al. . UAV-spray application in vineyards: flight modes and spray system adjustment effects on canopy deposit, coverage, and off-target losses. Sci Total Environ, 2022, 845: 157292

[12] Bondre S, Patil D. Recent advances in agricultural disease image recognition technologies: a review. Concurrency Comput Pract Exp, 2023, 35: e7644

[13] Calou VBC, Teixeira A dos S, Moreira LCJ, et al. The use of UAVs in monitoring yellow sigatoka in banana. Biosyst Eng. 2020;193:115–125. https://doi.org/10.1016/j.biosystemseng.2020.02.016.

[14] Carrasco LR, Mumford JD, MacLeod A, et al. . Unveiling human-assisted dispersal mechanisms in invasive alien insects: integration of spatial stochastic simulation and phenology models. Ecol Model, 2010, 221: 2068-2075

[15] Castillo JM, Gallego-Tévar B, Figueroa E, et al. . Low genetic diversity contrasts with high phenotypic variability in heptaploid Spartina densiflora populations invading the Pacific coast of North America. Ecol Evol, 2018, 8: 4992-5007

[16] Cellini A, Blasioli S, Biondi E, et al. . Potential applications and limitations of electronic nose devices for plant disease diagnosis. Sensors, 2017, 17: 2596

[17] Chen D, Shi Y, Huang W, et al. . Mapping wheat rust based on high spatial resolution satellite imagery. Comput Electron Agric, 2018, 152: 109-116

[18] Chen S, Lan Y, Zhou Z, et al. . Effect of droplet size parameters on droplet deposition and drift of aerial spraying by using plant protection UAV. Agronomy, 2020, 10: 195

[19] Chen C, Ma Y, Ren G, Wang J. Aboveground biomass of salt-marsh vegetation in coastal wetlands: sample expansion of in situ hyperspectral and Sentinel-2 data using a generative adversarial network. Remote Sens Environ, 2022, 270: 112885

[20] Chen J, Wang Y, Cao T, et al. . Liang Q, Wang W, Liu X, et al. . Research on pesticide fog droplet drift detection applied in UAV. Communications, signal processing, and systems, 2022, Singapore, Springer: 1162-1167

[21] Chen Y, Liu Z, Lin Z, et al. UAV-UGV cooperative targeted spraying system for honey pomelo orchard. Int J Agric Biol Eng. 2024a; 17:22–31. https://doi.org/10.25165/ijabe.v17i6.8989.

[22] Chen Y, Qiao X, Qin F, et al. . IPMCNet: a lightweight algorithm for invasive plant multiclassification. Agronomy, 2024, 14: 333

[23] Cho S-B, Cheng Y, Sul S. Enhanced classification performance through GauGAN‐based data augmentation for tomato leaf images. IET Image Process, 2024, 18: 4887-4897

[24] Chowdhury S, Hamerly G, McGarrity M. Active learning strategy using contrastive learning and k-means for aquatic invasive species recognition. In: 2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW); 2024:848–858. https://doi.org/10.1109/wacvw60836.2024.00097.

[25] Cini A, Santosuosso U, Papini A. Uncovering the spatial pattern of invasion of the honeybee pest small hive beetle, Aethina tumida, in Italy. Rev Bras Entomol, 2019, 63: 12-17

[26] Dai J, Roberts DA, Stow DA, et al. . Mapping understory invasive plant species with field and remotely sensed data in Chitwan, Nepal. Remote Sens Environ, 2020, 250 112037

[27] Dao PD, Axiotis A, He Y. Mapping native and invasive grassland species and characterizing topography-driven species dynamics using high spatial resolution hyperspectral imagery. Int J Appl Earth Obs Geoinf, 2021, 104: 102542

[28] Deutsch CA, Tewksbury JJ, Tigchelaar M, et al. . Increase in crop losses to insect pests in a warming climate. Science, 2018, 361: 916-919

[29] dos Santos RP, Matos-Carvalho JP, Leithardt VRQ. Deep learning in time series forecasting with transformer models and RNNs. PeerJ Comput Sci, 2025, 11: e3001

[30] Eccles BJ, Rodgers P, Kilpatrick P, et al. . DNNShifter: an efficient DNN pruning system for edge computing. Future Gener Comput Syst, 2024, 152: 43-54

[31] Esfandi K, Afsar S, Richards K, et al. . Determining the efficacy of visual inspections at detecting non-biosecurity–compliant goods. J vis, 2024, 24: 8

[32] Estay SA, Silva CP, López DN, Labra FA. Disentangling the spread dynamics of insect invasions using spatial networks. Front Ecol Evol, 2023, 11: 1124890

[33] Farid AM, Mouhoub M, Arkles T, Hutch G. Filipe J, Röning J. Multi-UAV weed spraying. Robotics, computer vision and intelligent systems, 2024, Cham, Springer Nature Switzerland: 210-224

[34] Fernandes JMC, Nicolau M, Pavan W, et al. . A weather-based model for predicting early season inoculum build-up and spike infection by the wheat blast pathogen. Trop Plant Pathol, 2017, 42: 230-237

[35] Garcés-Gómez YA, Cabezas-Alzate DF, Henao-Céspedes V, Corpas-Iguarán EJ. Assessing the effects of water hyacinth proliferation on biochemical oxygen demand with operational land imager data. Phys Chem Earth Parts ABC, 2024, 136: 103733

[36] Garcia-Ruiz F, Sankaran S, Maja JM, et al. . Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees. Comput Electron Agric, 2013, 91: 106-115

[37] Garre P, Harish A. Autonomous agricultural pesticide spraying UAV. IOP Conf Ser Mater Sci Eng, 2018, 455 012030

[38] Gavel H, Azad C. Retinal disease detection for low resource devices through pruned and quantized CNNs. In: 2025 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS); 2025:1–6. https://doi.org/10.1109/sceecs64059.2025.10941284

[39] Godefroid M, Meurisse N, Groenen F, et al. . Current and future distribution of the invasive oak processionary moth. Biol Invasions, 2020, 22: 523-534

[40] Gonzalez-Carabarin L, Schmid A, Van Sloun RJG. Hardware-oriented pruning and quantization of deep learning models to detect life-threatening arrhythmias. In: 2021 IEEE Biomedical Circuits and Systems Conference (BioCAS); 2021:1–6. https://doi.org/10.1109/biocas49922.2021.9644976.

[41] Gorvadiya J, Chagela A, Roy M. Energy efficient pruning and quantization methods for deep learning models. In: 2025 International Conference on Sustainable Energy Technologies and Computational Intelligence (SETCOM); 2025: 1–6. https://doi.org/10.1109/setcom64758.2025.10932458.

[42] Gruntman M, Segev U, Tielbörger K. Shade-induced plasticity in invasive Impatiens glandulifera populations. Weed Res, 2020, 60: 16-25

[43] Guo Z, Cai D, Bai J, et al. . Intelligent rice field weed control in precision agriculture: from weed recognition to variable rate spraying. Agronomy, 2024, 14: 1702

[44] Guo Y, Mokany K, Levick SR, et al. . Spatioformer: a geo-encoded transformer for large-scale plant species richness prediction. IEEE Trans Geosci Remote Sens, 2025, 63: 1-16

[45] Hasan W, Mahesh, Sampat Pagire K, et al. Climate-resilient pest management: integration of pest management practices with pest range shifts, heat stress biology and cropping system adaptation. Int J Environ Clim Change. 2026; 16:528–545. https://doi.org/10.9734/ijecc/2026/v16i15253.

[46] Huang K, Li K, Shu L, et al. . High voltage discharge exhibits severe effect on ZigBee-based device in solar insecticidal lamps internet of things. IEEE Wirel Commun, 2020, 27: 140-145

[47] Huang Z, Xu Z, Li Y, et al. . Computer vision-based biomass estimation for invasive plants. J vis Exp, 2024, 204: e66067

[48] Ispizua Yamati FR, Bömer J, Noack N, et al. . Configuration of a multisensor platform for advanced plant phenotyping and disease detection: case study on Cercospora leaf spot in sugar beet. Smart Agric Technol, 2025, 10: 100740

[49] Jiang P, Chen Y, Liu B, et al. . Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access, 2019, 7: 59069-59080

[50] Jing Q, Yu-man N, Yong-ping W, et al. Dynamic features extraction system of live pests in farmland. In: 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC); 2018:1141–1146. https://doi.org/10.1109/yac.2018.8406543.

[51] Karkee M, Adhikari B, Amatya S, Zhang Q. Identification of pruning branches in tall spindle apple trees for automated pruning. Comput Electron Agric, 2014, 103: 127-135

[52] Karkee M, Zhang Q, Silwal A. Bechar A. Agricultural robots for precision agricultural tasks in tree fruit orchards. Innovation in agricultural robotics for precision agriculture: a roadmap for integrating robots in precision agriculture, 2021, Cham, Springer International Publishing: 63-89

[53] Kattenborn T, Lopatin J, Förster M, et al. . UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data. Remote Sens Environ, 2019, 227: 61-73

[54] Keasar T, Wajnberg E, Heimpel G, et al. . Dynamic economic thresholds for insecticide applications against agricultural pests: importance of pest and natural enemy migration. J Econ Entomol, 2023, 116: 321-330

[55] Khan R, Belhadj S, Khan A, et al. . Singh R, Bhadouria R, Tripathi S, et al. . Ecosystem services and disservices linked to invasive alien plants. Invasive alien plants in urban ecosystems: emerging concerns and socio-ecological dimensions, 2025, Cham, Springer Nature Switzerland: 155-176

[56] Khwidzhilli RH, Ijatuyi EJ. Leveraging digital tools for sustainable precision in agriculture 3.0–5.0: a scoping review of trends, benefits, and challenges. Front Sustain Food Syst. 2026;10:1768902. https://doi.org/10.3389/fsufs.2026.1768902.

[57] Kopeć D, Zakrzewska A, Halladin-Dąbrowska A, et al. The essence of acquisition time of airborne hyperspectral and on-ground reference data for classification of highly invasive annual vine Echinocystis lobata (Michx.) Torr.&A. Gray. GIScience Remote Sens. 2023;60:2204682. https://doi.org/10.1080/15481603.2023.2204682.

[58] Koumoutsou D, Siolas G, Charou E, Stamou G. Razavi-Far R, Ruiz-Garcia A, Palade V, Schmidhuber J. Generative adversarial networks for data augmentation in hyperspectral image classification. Generative adversarial learning: architectures and applications, 2022, Cham, Springer International Publishing: 115-144

[59] Kumar M, Kumar A, Palaparthy V. Soil sensors-based prediction system for plant diseases using exploratory data analysis and machine learning. IEEE Sens J, 2021, 21: 17455-17468

[60] Lehnert C, McCool C, Sa I, Perez T. Performance improvements of a sweet pepper harvesting robot in protected cropping environments. J Field Robot, 2020, 37: 1197-1223

[61] Li L, Zhang S, Wang B. Plant disease detection and classification by deep learning—a review. IEEE Access, 2021, 9: 56683-56698

[62] Li W, Duveiller G, Wieneke S, et al. . Regulation of the global carbon and water cycles through vegetation structural and physiological dynamics. Environ Res Lett, 2024, 19: 073008

[63] Li Z-X, Tan J-F, Yao N, Xie R-H. From trade-off to synergy: how nutrient status modulates plant resistance to herbivorous insects?. Adv Biotechnol, 2024, 2: 37

[64] Lian Q, Tan F, Fu X, et al. Design of precision variable-rate spray system for unmanned aerial vehicle using automatic control method. Int J Agric Biol Eng. 2019;12:29–35. https://doi.org/10.25165/j.ijabe.20191202.4701.

[65] Liang T, Glossner J, Wang L, et al. . Pruning and quantization for deep neural network acceleration: a survey. Neurocomputing, 2021, 461: 370-403

[66] Lin Q, Huang H, Chen L, et al. . Using the 3D model RAPID to invert the shoot dieback ratio of vertically heterogeneous Yunnan pine forests to detect beetle damage. Remote Sens Environ, 2021, 260: 112475

[67] Ling X, Zhao Y, Gong L, et al. . Dual-arm cooperation and implementing for robotic harvesting tomato using binocular vision. Robot Auton Syst, 2019, 114: 134-143

[68] Liu J, Wang X, Chen Q, et al. . Deep learning method for cucumber disease detection in complex environments for new agricultural productivity. BMC Plant Biol, 2025, 25: 888

[69] Liu F, Zhao S-B, Liu Y-H, et al. . Machine learning-driving optimization and spatial assembly of a cell-free system for high-yield liquiritigenin production. Adv Biotechnol, 2026, 4: 12

[70] Lottes P, Behley J, Milioto A, Stachniss C. Fully convolutional networks with sequential information for robust crop and weed detection in precision farming. IEEE Robot Autom Lett, 2018, 3: 2870-2877

[71] Lottes P, Stachniss C. Semi-supervised online visual crop and weed classification in precision farming exploiting plant arrangement. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2017:5155–5161. https://doi.org/10.1109/iros.2017.8206403.

[72] Ma H, Huang W, Jing Y, et al. . Integrating growth and environmental parameters to discriminate powdery mildew and aphid of winter wheat using bi-temporal Landsat-8 imagery. Remote Sens, 2019, 11: 846

[73] Ma G, Ma C-S. Potential distribution of invasive crop pests under climate change: incorporating mitigation responses of insects into prediction models. Curr Opin Insect Sci, 2022, 49: 15-21

[74] Meng Y, Su J, Song J, et al. . Experimental evaluation of UAV spraying for peach trees of different shapes: effects of operational parameters on droplet distribution. Comput Electron Agric, 2020, 170: 105282

[75] Miller T, Mikiciuk G, Durlik I, et al. . The IoT and AI in agriculture: the time is now—a systematic review of smart sensing technologies. Sensors (Basel), 2025, 25: 3583

[76] Min W, Zhai M, Chen S, et al. . Mobile acoustic net: a novel early detection model for wood-boring pests. Comput Electron Agric, 2025, 229: 109699

[77] Monteleone B, Borzí I, Bonaccorso B, Martina M. Quantifying crop vulnerability to weather-related extreme events and climate change through vulnerability curves. Nat Hazards, 2023, 116: 2761-2796

[78] Muluneh MG. Impact of climate change on biodiversity and food security: a global perspective—a review article. Agric Food Secur, 2021, 10: 36

[79] Ninatanta C, Cole R, Wells I, et al. Design and evaluation of a lightweight soft electrical apple harvesting gripper. In: 2024 IEEE 7th International Conference on Soft Robotics (RoboSoft); 2024: 479–484. https://doi.org/10.1109/RoboSoft60065.2024.10521995.

[80] Nnamdi UV, Abolghasemi V. Optimised MobileNet for very lightweight and accurate plant leaf disease detection. Sci Rep, 2025, 15: 43690

[81] Pandey S, Singh S, Tyagi V, et al. . Singh M, Tyagi V, Gupta PK, et al. . Meta-learning for few-shot insect pest detection in rice crop. Advances in computing and data sciences, 2022, Cham, Springer International Publishing: 404-414

[82] Pandya D, Jain K. A review on crop disease classification and prevention of pest using deep transfer ensemble learning in agriculture. In: 2023 IEEE International Conference on ICT in Business Industry&Government (ICTBIG); 2023:1–12. https://doi.org/10.1109/ictbig59752.2023.10456014.

[83] Prasad AM, Iverson LR, Peters MP, et al. . Modeling the invasive emerald ash borer risk of spread using a spatially explicit cellular model. Landsc Ecol, 2010, 25: 353-369

[84] Pretto A, Aravecchia S, Burgard W, et al. . Building an aerial–ground robotics system for precision farming: an adaptable solution. IEEE Robot Autom Mag, 2021, 28: 29-49

[85] Qi H, Lin Z, Zhou J, et al. Effect of temperature and humidity on droplet deposition of unmanned agricultural aircraft system. Int J Precis Agric Aviat. 2020; 3:41–49. https://doi.org/10.33440/j.ijpaa.20200303.100.

[86] Qi F, Wang Y, Tang Z, Chen S. Real-time and effective detection of agricultural pest using an improved YOLOv5 network. J Real-Time Image Process, 2023, 20: 33

[87] Radoglou-Grammatikis P, Sarigiannidis P, Lagkas T, Moscholios I. A compilation of UAV applications for precision agriculture. Comput Netw, 2020, 172: 107148

[88] Ren Z, Liang K, Liu Y, et al. . A neural network with multiscale convolution and feature attention based on an electronic nose for rapid detection of common bunt disease in wheat plants. Agriculture, 2025, 15: 415

[89] Rincon DF, Rivera-Trujillo HF, Borrero-Echeverry F. A real-time decision-making tool based on dynamic thresholds for Phthorimaea absoluta management in greenhouse tomato. Crop Prot, 2023, 167: 106196

[90] Roy PS, Kukreja V. An analytical study of assorted and resilient rice blast varieties using deep learning techniques. In: 2024 Global Conference on Communications and Information Technologies (GCCIT); 2024:1–6. https://doi.org/10.1109/gccit63234.2024.10862610.

[91] Ryo M. Explainable artificial intelligence and interpretable machine learning for agricultural data analysis. Artif Intell Agric, 2022, 6: 257-265

[92] Saravana MK, Roopa MS, Arunalatha JS, Venugopal KR. Transformers for multivariate time series forecasting: comprehensive analysis, challenges, research opportunities, and future prospects. IEEE Access, 2026, 14: 11424-11457

[93] Sarkar SK, Das J, Ehsani R, Kumar V. Towards autonomous phytopathology: outcomes and challenges of citrus greening disease detection through close-range remote sensing. In: 2016 IEEE International Conference on Robotics and Automation (ICRA); 2016:5143–5148. https://doi.org/10.1109/icra.2016.7487719.

[94] Skendžić S, Zovko M, Živković IP, et al. . The impact of climate change on agricultural insect pests. Insects, 2021, 12 440

[95] Sobiech Ł, Grzanka M, Skrzypczak G, et al. . Effect of adjuvants and pH adjuster on the efficacy of sulcotrione herbicide. Agronomy, 2020, 10: 530

[96] Song R, Shen G, Liu Y, et al. . Preparation and characterization of an oil-in-water microemulsion of thiamethoxam and acetamiprid without organic solvent for unmanned aerial vehicle spraying. Colloids Surf Physicochem Eng Asp, 2020, 607: 125485

[97] Stoeckli S, Felber R, Haye T. Current distribution and voltinism of the brown marmorated stink bug, Halyomorpha halys, in Switzerland and its response to climate change using a high-resolution CLIMEX model. Int J Biometeorol, 2020, 64: 2019-2032

[98] Sun J, Wang Q, Li Q, et al. . Artificial intelligence in smart agriculture across the production-to-postharvest continuum: progress, challenges, and future directions. Sustainability, 2026, 18: 4908

[99] Tang Y, Hou CJ, Luo SM, et al. . Effects of operation height and tree shape on droplet deposition in citrus trees using an unmanned aerial vehicle. Comput Electron Agric, 2018, 148: 1-7

[100] Tang L, Zhou QS, Gao Y, Li P. Biomass allocation in response to salinity and competition in native and invasive species. Ecosphere, 2022, 13: e3900

[101] Taylor B, Casey J, Annamalai S, et al. Minimizing pest and disease risks in uncertain climates: CABI initiatives developing new technologies and tools for outreaching early warning to farmers. agriRxiv. 2021;2021:20210271298. https://doi.org/10.31220/agriRxiv.2021.00064.

[102] Tian Y, Jia M, Wang Z, et al. . Monitoring invasion process of Spartina alterniflora by seasonal Sentinel-2 imagery and an object-based random forest classification. Remote Sens, 2020, 12: 1383

[103] Tripathy AK, Adinarayana J, Sudharsan D, et al. Data mining and wireless sensor network for agriculture pest/disease predictions. In: 2011 World Congress on Information and Communication Technologies; 2011:1229–1234. https://doi.org/10.1109/wict.2011.6141424.

[104] Tzachor A, Devare M, King B, et al. . Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities. Nat Mach Intell, 2022, 4: 104-109

[105] Upadhyay A, Chandel NS, Singh KP, et al. . Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture. Artif Intell Rev, 2025, 58: 92

[106] Verma A, Singh M, Parmar RP, Bhullar KS. Feasibility study on hexacopter UAV based sprayer for application of environment-friendly biopesticide in guava orchard. J Environ Biol. 2022;43:97–104. https://doi.org/10.22438/eb/43/1/MRN-1912.

[107] Vrochidou E, Tziridis K, Nikolaou A, et al. . An autonomous grape-harvester robot: integrated system architecture. Electronics, 2021, 10: 1056

[108] Wadmare J, Joisar J, Jadav M, Joshi S. Comparative analysis of disease detection among tomato plants using machine and deep learning techniques. In: 2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP); 2024: 357–361. https://doi.org/10.1109/innocomp63224.2024.00066.

[109] Wan F-H, Yang N-W. Invasion and management of agricultural alien insects in China. Annu Rev Entomol, 2016, 61: 77-98

[110] Wang J, Lan Y, Zhang H, et al. Drift and deposition of pesticide applied by UAV on pineapple plants under different meteorological conditions. Int J Agric Biol Eng. 2018;11:5–12. https://doi.org/10.25165/ijabe.v11i6.4038.

[111] Wang G, Han Y, Li X, et al. . Field evaluation of spray drift and environmental impact using an agricultural unmanned aerial vehicle (UAV) sprayer. Sci Total Environ, 2020, 737: 139793

[112] Wang C, Herbst A, Zeng A, et al. . Assessment of spray deposition, drift and mass balance from unmanned aerial vehicle sprayer using an artificial vineyard. Sci Total Environ, 2021, 777: 146181

[113] Wei P, Yan X, Yan W, et al. . Precise extraction of targeted apple tree canopy with YOLO-Fi model for advanced UAV spraying plans. Comput Electron Agric, 2024, 226: 109425

[114] Wu X, Deng H, Wang Q, et al. . Meta-learning shows great potential in plant disease recognition under few available samples. Plant J, 2023, 114: 767-782

[115] XianYi C, ZhiGang J, Xiong Y. Design of tropical crops pests monitoring system based on wireless sensor network. In: 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet); 2012:2530–2532. https://doi.org/10.1109/cecnet.2012.6202138.

[116] Xing F, An R, Guo X, Shen X. Mapping invasive noxious weed species in the alpine grassland ecosystems using very high spatial resolution UAV hyperspectral imagery and a novel deep learning model. Giscience Remote Sens, 2024, 61: 2327146

[117] Xu M, Yoon S, Jeong Y, Park DS. Transfer learning for versatile plant disease recognition with limited data. Front Plant Sci, 2022, 13: 1010981

[118] Xue X, Luo Q, Ji Y, et al. . Design and test of Kinect-based variable spraying control system for orchards. Front Plant Sci, 2023, 14: 1297879

[119] Yallappa D, Kavitha R, Surendrakumar A, et al. . Effect of downwash airflow distribution of multi-rotor unmanned aerial vehicle on spray droplet deposition characteristics in rice crop. Curr Sci, 2023, 125: 172-182

[120] Yang C-M. Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance. Precis Agric, 2010, 11: 61-81

[121] Yu H, Li X, Yu Y, et al. . A dual-branch multimodal model for early detection of rice sheath blight: fusing spectral and physiological signatures. Comput Electron Agric, 2025, 231: 110031

[122] Zhan Y, Chen S, Wang G, et al. . Biological control technology and application based on agricultural unmanned aerial vehicle (UAV) intelligent delivery of insect natural enemies (Trichogramma) carrier. Pest Manag Sci, 2021, 77: 3259-3272

[123] Zhang M, Qin Z, Liu X. Remote sensed spectral imagery to detect late blight in field tomatoes. Precis Agric, 2005, 6: 489-508

[124] Zhang J, Huang Y, Pu R, et al. . Monitoring plant diseases and pests through remote sensing technology: a review. Comput Electron Agric, 2019, 165: 104943

[125] Zhang B, Ye H, Lu W, et al. . A spatiotemporal change detection method for monitoring pine wilt disease in a complex landscape using high-resolution remote sensing imagery. Remote Sens, 2021, 13: 2083

[126] Zhang H, He B, Xing J, Lu M. Deep spatial and temporal graph convolutional network for rice planthopper population dynamic forecasting. Comput Electron Agric, 2023, 210: 107868

[127] Zhang Q, Luo X, Hu L, et al. . Method and experiments for acquiring high spatial resolution images of abnormal rice canopy by autonomous unmanned aerial vehicle field inspection. Agronomy, 2023, 13: 2731

[128] Zhang Z, Ma L, Wei C, et al. . Cotton Fusarium wilt diagnosis based on generative adversarial networks in small samples. Front Plant Sci, 2023, 14: 1290774

[129] Zhang X, Vinatzer BA, Li S. Hyperspectral imaging analysis for early detection of tomato bacterial leaf spot disease. Sci Rep, 2024, 14: 27666

[130] Zhao J, Guo J, Liu C, et al. Monitoring of powdery mildew on winter wheat using multi-temporal HJ-CCD imagery on a regional scale. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS); 2017:5085–5088. https://doi.org/10.1109/igarss.2017.8128146.

[131] Zhao H, Zhong Y, Wang X, et al. . Mapping the distribution of invasive tree species using deep one-class classification in the tropical montane landscape of Kenya. ISPRS J Photogramm Remote Sens, 2022, 187: 328-344

[132] Zheng Q, Huang W, Cui X, et al. . New spectral index for detecting wheat yellow rust using Sentinel-2 multispectral imagery. Sensors, 2018, 18: 868

[133] Zhu H, Chu B, Zhang C, et al. . Hyperspectral imaging for presymptomatic detection of tobacco disease with successive projections algorithm and machine-learning classifiers. Sci Rep, 2017, 7: 4125

Funding

Guangdong Basic and Applied Basic Research Foundation(2024A1515010909)

RIGHTS & PERMISSIONS

The Author(s)