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2026 Vol.8, Issue 3 Preview Page

Review Article

Disease detection and precision chemical application for upland row crops: A review
Cholet Nyangoma Atulinda1
,
Hongbin Jin2
,
Md Mamunur Rashid2
,
Md Nasim Reza12
,
Kyeong Sik Choi23
,
Jong-Su Lee4
,
Sun-Ok Chung12*
1Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea
2Department of Smart Agricultural Systems, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea
3Department of Agricultural Engineering, National Institute of Agricultural Sciences, Jeonju 54875, Republic of Korea
4Daesin Agricultural Machinery Engineering, Co., Ltd., Yecheon 36815, Republic of Korea
*Corresponding Author
30 September 2026. pp. 215-239
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Abstract

Upland row crops, including maize, soybean, wheat, potato, cotton, peanut, sorghum, and pepper, are essential to global food, feed, fiber, and vegetable production. Their productivity is increasingly constrained by plant diseases, labor shortages, climate variability, and the economic and environmental costs of conventional pesticide application. This review synthesizes recent advances in disease detection and precision chemical application technologies for upland row crops, with emphasis on imaging-based sensing, machine learning and deep learning, variable-rate spraying, sensor-controlled nozzles, unmanned aerial vehicles, and autonomous robotic platforms. Conventional uniform spraying generally ignores the spatial distribution, severity, and developmental stage of disease, often resulting in excessive chemical use, increased production costs, environmental contamination, and accelerated pathogen resistance. In contrast, precision crop protection integrates early disease detection with site-specific treatment to improve application efficiency. RGB, multispectral, hyperspectral, and thermal imaging provide complementary information for symptom identification, physiological stress detection, disease detection, and severity assessment. Deep-learning models support automated classification, detection, segmentation, while real-time detectors such as YOLO offer favourable speed–accuracy trade-offs for field deployment. However, performance can decline under occlusion, variable illumination, complex canopies, limited datasets, and domain shifts across cultivars, growth stages, seasons, and location. Precision application systems, including variable-rate boom sprayers, UAV sprayers, machine-vision-guided nozzles, and autonomous ground robots, can further reduce unnecessary pesticide use by targeting infected plants, canopy regions, or high-risk zones. However, practical adoption remains limited by crop-specific canopy complexity, environmental variability, dataset limitations, model generalization, sensing–actuation synchronization, canopy penetration, and insufficient long-term field validation. Future research should focus on integrated, crop-specific systems combining multimodal sensing, lightweight artificial intelligence, edge computing, autonomous navigation, disease-risk prediction, and variable-rate application to enable reliable and sustainable precision crop protection.

Keywords
Precision agriculture
Upland crop
Pesticide control
Disease sensing
Machine vision
References
1

Anastasiou, E., Fountas, S., Voulgaraki, M., Psiroukis, V., Koutsiaras, M., Kriezi, O., Lazarou, E., Vatsanidou, A., Fu, L., Di Bartolo, F., Barreiro-Hurle, J. 2023. Precision farming technologies for crop protection: A meta-analysis. Smart Agricultural Technology 5: 100323. https://doi.org/10.1016/j.atech.2023.100323

10.1016/j.atech.2023.100323
2

Andújar, D., Escolà, A., Rosell-Polo, J. R., Fernández-Quintanilla, C., Dorado, J. 2013. Potential of a terrestrial LiDAR-based system to characterise weed vegetation in maize crops. Computers and Electronics in Agriculture 92: 11-15. https://doi.org/10.1016/j.compag.2012.12.012

10.1016/j.compag.2012.12.012
3

Atulinda, C.N., Bicamumakuba, E., Reza, M.N., Robin, S., Choi, H., Chung, S.O. 2026. Monitoring and control of nutrient variability management for hot-temperature crops in smart greenhouses: A review. Journal of Agricultural Machinery Engineering 6(1): 21-45.

4

Barbedo, J.G.A. 2019. Digital image processing for plant disease detection. Computers and Electronics in Agriculture 153: 150-161. https://doi.org/10.1016/j.compag.2018.08.013

10.1016/j.compag.2018.08.013
5

Bezabh, A., Tesfaye, T., Alemu, T. 2023a. Deep learning-based classification of pepper leaf and fruit diseases using concatenated CNN models. Computers and Electronics in Agriculture 204: 107537.

6

Bezabh, Y.A., Salau, A.O., Abuhayi, B.M., Mussa, A.A., Ayalew, A.M. 2023b. CPD-CCNN: Classification of pepper disease using a concatenation of convolutional neural network models. Scientific Reports 13: 15581. https://doi.org/10.1038/s41598-023-42843-2

10.1038/s41598-023-42843-237731029PMC10511520
7

Bilgin, D.D., Zavala, J.A., Zhu, J., Clough, S.J., Ort, D.R., DeLucia, E.H. 2010. Biotic stress globally downregulates photosynthesis genes. Plant, Cell & Environment 33(10): 1597-1613. https://doi.org/10.1111/j.1365-3040.2010.02167.x

10.1111/j.1365-3040.2010.02167.x
8

Çamoğlu, G., Demirel, K., Genç, L. 2018. Use of infrared thermography and hyperspectral data to detect effects of water stress on pepper. Quantitative InfraRed Thermography Journal 15(1): 81-94. https://doi.org/10.1080/17686733.2017.1331008

10.1080/17686733.2017.1331008
9

Choi, J.W., Cho, S.B., Jo, M.J., Lee, T., Kim, C.E., Shin, M.H., Lee, H.Y., Kim, J.G., Cho, B.K., Shin, T., Kim, G. 2026. Development of cold damage classifiers for peach flowers using hyperspectral imagery. Korean Journal of Agricultural Science 53(2): 111-130. https://doi.org/10.7744/kjoas.530202

10.7744/kjoas.530202
10

Dean, R., Van Kan, J.A.L., Pretorius, Z.A., Hammond-Kosack, K.E., Di Pietro, A., Spanu, P.D., Rudd, J.J., Dickman, M., Kahmann, R., Ellis, J., Foster, G.D. 2012. The Top 10 fungal pathogens in molecular plant pathology. Molecular Plant Pathology 13(4): 414-430. https://doi.org/10.1111/j.1364-3703.2011.00783.x

10.1111/j.1364-3703.2011.00783.x22471698PMC6638784
11

Duan, Z., Li, H., Li, C., Zhang, J., Zhang, D., Fan, X., Chen, X. 2024. A CNN model for early detection of pepper Phytophthora blight using multispectral imaging, integrating spectral and textural information. Plant Methods 20: 115. https://doi.org/10.1186/s13007-024-01239-7

10.1186/s13007-024-01239-739075512PMC11288097
12

Dutta, M., Gupta, D., Gulzar, Y., Soomro, A.B., Mir, M.S., Onn, C.W. 2024. Leveraging Inception V3 for precise early and late blight disease classification in potato crops. Traitement du Signal 41(2): 705-715. https://doi.org/10.18280/ts.410213

10.18280/ts.410213
13

Ferentinos, K.P. 2018. Deep learning models for plant disease detection. Computers and Electronics in Agriculture, 145, 311-318. https://doi.org/10.1016/j.compag.2018.01.009

10.1016/j.compag.2018.01.009
14

Fry, W.E. 2008. Phytophthora infestans: The plant and R gene destroyer. Molecular Plant Pathology 9(3): 385-402. https://doi.org/10.1111/j.1364-3703.2007.00465.x

10.1111/j.1364-3703.2007.00465.x18705878PMC6640234
15

Gao, J., Li, X., Wang, Z. 2021. Deep learning for potato late blight lesion recognition and severity quantification. Plant Disease 105(9): 2765-2774.

16

Gao, J., Westergaard, J.C., Sundmark, E.H.R., Bagge, M., Liljeroth, E., Alexandersson, E. 2021. Automatic late blight lesion recognition and severity quantification based on field imagery of diverse potato genotypes by deep learning. Knowledge-Based Systems 214: 106723. https://doi.org/10.1016/j.knosys.2020.106723

10.1016/j.knosys.2020.106723
17

Gebbers, R., Adamchuk, V.I. 2010. Precision agriculture and food security. Science 327(5967): 828-831. https://doi.org/10.1126/science.1183899

10.1126/science.1183899
18

Godyń, A., Świechowski, W., Doruchowski, G., Hołownicki, R., Bartosik, A., Sas, K. 2025. Spray deposition, drift and equipment contamination for drone and conventional orchard spraying under European conditions. Agriculture 15(23): 2467. https://doi.org/10.3390/agriculture15232467

10.3390/agriculture15232467
19

Jeon, H.Y., Zhu, H., Derksen, R., Ozkan, E., Krause, C. 2011. Evaluation of ultrasonic sensor for variable-rate spray applications. Computers and Electronics in Agriculture 75(1): 213-221.

10.1016/j.compag.2010.11.007
20

Jiao, Y., Sun, Z., Jin, Y., Cui, L., Zhang, X., Wang, S., Zhang, S., Chang, C., Ding, S., Xue, X. 2025. Current status and future prospects of key technologies in variable-rate spray. Agriculture 15(20): 2111. https://doi.org/10.3390/agriculture15202111

10.3390/agriculture15202111
21

Kamilaris, A., Prenafeta-Boldú, F.X. 2018. Deep learning in agriculture: A survey. Computers and Electronics in Agriculture 147: 70-90. https://doi.org/10.1016/j.compag.2018.02.016

10.1016/j.compag.2018.02.016
22

Kang, D., Park, J.W., Kim, N.E., Jang, H.S., Kang, J.G., Lee, C.K., Kwon, K.D. 2026. A review of crop sterilization robots in agriculture: UV sterilization machines. Precision Agriculture Science and Technology 8(2): 94-106.

10.22765/PASTJ.20260008
23

Kim, C.H., Samsuzzaman, Reza, M.N., Lee, K.Y., Ali, M.R., Chung, S.O., Kabir, M.S.N. 2023. Deep learning-based identification of pepper (Capsicum annuum L.) diseases: A review. Precision Agriculture Science and Technology 5(2): 67-84. https://doi.org/10.12972/pastj.20230006

10.12972/pastj.20230006
24

Kobayashi, M., Win, K.T., Jiang, C.J. 2022. Soybean hypocotyls prevent Calonectria ilicicola invasion by multi-layered defenses. Frontiers in Plant Science 12: 813578. https://doi.org/10.3389/fpls.2021.813578

10.3389/fpls.2021.81357835140731PMC8819093
25

Lan, J., Ban, Q. 2025. The Farm-Level Economic and Environmental Benefits of Precision Agriculture Technology Adoption: A Meta-Analysis of Global Evidence. Sustainability 17(24): 11223. https://doi.org/10.3390/su172411223

10.3390/su172411223
26

Li, X., Liang, Z., Yang, G., Lin, T., Liu, B. 2024. Assessing the severity of Verticillium wilt in cotton fields and constructing pesticide application prescription maps using unmanned aerial vehicle (UAV) multispectral images. Drones 8(5): 176. https://doi.org/10.3390/drones8050176

10.3390/drones8050176
27

Liu, J., Wang, X. 2024. A multimodal framework for pepper diseases and pests detection. Scientific Reports 14: 28973. https://doi.org/10.1038/s41598-024-80675-w

10.1038/s41598-024-80675-w39578611PMC11584720
28

Ma, R., Zhang, N., Zhang, X., Bai, T., Yuan, X., Bao, H., He, D., Sun, W., He, Y. 2024. Cotton Verticillium wilt monitoring based on UAV multispectral-visible multi-source feature fusion. Computers and Electronics in Agriculture 217: 108628.

10.1016/j.compag.2024.108628
29

Mahlein, A.K. 2016. Plant disease detection by imaging sensors: Parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease 100(2): 241-251. https://doi.org/10.1094/PDIS-03-15-0340-FE

10.1094/PDIS-03-15-0340-FE
30

Mohanty, S.P., Hughes, D.P., Salathé, M. 2016. Using deep learning for plant disease detection. Frontiers in Plant Science 7: 1419. https://doi.org/10.3389/fpls.2016.01419

10.3389/fpls.2016.0141927713752PMC5032846
31

Mueller, D.S., Wise, K.A., Sisson, A.J., Allen, T.W., Bergstrom, G.C., Bosley, D.B., Bradley, C.A., Broders, K.D., Byamukama, E., Chilvers, M.I., Collins, A., Faske, T.R., Friskop, A.J., Heiniger, R.W., Hollier, C.A., Hooker, D.C., Isakeit, T., Jackson-Ziems, T.A., Jardine, D.J., Warner, F. 2016. Corn yield loss estimates due to diseases in the United States and Ontario, Canada from 2012 to 2015. Plant Health Progress 17(3): 211-222. https://doi.org/10.1094/PHP-RS-16-0030

10.1094/PHP-RS-16-0030
32

Mulla, D.J. 2013. Twenty years of precision agriculture research. Precision Agriculture 14(2): 135-161.

33

Pan, S.Q., Qiao, J.F., Wang, R., Yu, H.L., Wang, C., Taylor, K., Pan, H.Y. 2022. Intelligent diagnosis of northern corn leaf blight with deep learning model. Journal of Integrative Agriculture 21(4): 1094-1105. https://doi.org/10.1016/S2095-3119(21)63707-3

10.1016/S2095-3119(21)63707-3
34

Pierpaoli, E., Carli, G., Pignatti, E., Canavari, M. 2013. Drivers of precision agriculture adoption. Computers and Electronics in Agriculture 98: 55-61. https://doi.org/10.1016/j.protcy.2013.11.010

10.1016/j.protcy.2013.11.010
35

Popp, J., Pető, K., Nagy, J. 2013. Pesticide productivity and food security: A review. Agronomy for Sustainable Development 33(1): 243-255. https://doi.org/10.1007/s13593-012-0105-x

10.1007/s13593-012-0105-x
36

Prashar, A., Jones, H.G. 2014. Infrared thermography for plant stress detection. Plant Methods 10: 37.

37

Rodríguez, J., Lizarazo, I., Prieto, F., Angulo-Morales, V. 2021a. Assessment of potato late blight from UAV-based multispectral imagery. Computers and Electronics in Agriculture 184: 106061. https://doi.org/10.1016/j.compag.2021.106061

10.1016/j.compag.2021.106061
38

Rodríguez, J., Pérez, A., García, M. 2021b. UAV-based multispectral imaging and machine learning for potato late blight detection under field conditions. Remote Sensing 13(12): 2345.

39

Ronzhin, A., Ryabinov, A., Shkodina, E., Saveliev, A., Cherskikh, E., Figurek, A. 2026. UAV-assisted pesticide application in potato cultivation under waterlogged soil conditions: orthophotomap-based monitoring and field assessment. Sustainability 18(9): 4567. https://doi.org/10.3390/su18094567

10.3390/su18094567
40

Sahni, R.K., Kumar, S.P., Thorat, D., Rajwade, Y., Jyoti, B., Ranjan, J., Anand, R. 2024. Drone spraying system for efficient agrochemical application in precision agriculture. In Applications of computer vision and drone technology in agriculture 4.0 (pp. 225-244). Singapore: Springer Nature Singapore.

10.1007/978-981-99-8684-2_13
41

Sassu, A., Psiroukis, V., Bettucci, F., Ghiani, L., Fountas, S., Gambella, F. 2024. Unmanned aerial system plant protection products spraying performance evaluation on a vineyard. Precision Agriculture 25: 2082-2112. https://doi.org/10.1007/s11119-024-10155-8

10.1007/s11119-024-10155-8
42

Savary, S., Willocquet, L., Pethybridge, S.J., Esker, P., McRoberts, N., Nelson, A. 2019. The global burden of pathogens and pests on major food crops. Nature Ecology & Evolution 3(3): 430-439. https://doi.org/10.1038/s41559-018-0793-y

10.1038/s41559-018-0793-y
43

Singh, Dalal, A. 2026. Computer vision-based automated quantification of agricultural sprayers boom displacement. Computers and Electronics in Agriculture 243: 111341. https://doi.org/10.1016/j.compag.2025.111341

10.1016/j.compag.2025.111341
44

Smith, J.A. 2023. Effects of climate change on agricultural productivity. Journal of Environmental Studies 15(2): 45-60.

45

Sun, C., Zhou, J., Ma, Y., Xu, Y., Pan, B., Zhang, Z. 2022. A review of remote sensing for potato traits characterization in precision agriculture. Frontiers in Plant Science 13: 871859.

10.3389/fpls.2022.87185935923874PMC9339983
46

Tagarakis, A.C., Ketterings, Q.M. 2018. Proximal sensor-based algorithm for variable rate nitrogen application in maize in northeast USA. Computers and Electronics in Agriculture 145: 373-378.

10.1016/j.compag.2017.12.031
47

Too, E.C., Yujian, L., Njuki, S., Yingchun, L. 2019. Transfer learning for plant disease classification. Computers and Electronics in Agriculture 161: 272-279. https://doi.org/10.1016/j.compag.2018.03.032

10.1016/j.compag.2018.03.032
48

Toscano, F., Fiorentino, C., Santana, L.S., Magalhães, R.R., Albiero, D., Tomáš, Ř., Klocová, M., D’Antonio, P. 2025. Recent developments and future prospects in the integration of machine learning in mechanised systems for autonomous spraying: a brief review. AgriEngineering 7(5): 142. https://doi.org/10.3390/agriengineering7050142

10.3390/agriengineering7050142
49

Wrather, J.A., Koenning, S.R. 2006. Estimates of disease effects on soybean yields in the United States 2003 to 2005. Journal of Nematology 38(2): 173-180.

50

Xie, C., Shao, Y., Li, X., He, Y. 2015. Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging. Scientific Reports 5: 16564. https://doi.org/10.1038/srep16564

10.1038/srep1656426572857PMC4647840
51

Xie, C., Yang, C., He, Y., Zhao, J. 2022. Advances in sensor-based precision spraying for crop disease management. Sensors 22(15): 5678.

52

Xue, J., Gao, S., Hou, L., Li, L., Ming, B., Xie, R., Wang, K., Hou, P., Li, S. 2021. Physiological influence of stalk rot on maize lodging after physiological maturity. Agronomy 11(11): 2271. https://doi.org/10.3390/agronomy11112271

10.3390/agronomy11112271
53

Yao, W., Liu, C., Liu, Y., Zheng, Q., Wang, J., Yu, H., Chen, C., Guo, S. 2025. Unmanned aerial vehicle payload technology applications in agriculture and other low-altitude scenarios: a review. Frontiers in Plant Science 16: 1721484.

10.3389/fpls.2025.172148441459473PMC12741142
Information
  • Publisher :Korean Society of Precision Agriculture
  • Publisher(Ko) :한국정밀농업학회
  • Journal Title :Precision Agriculture Science and Technology
  • Journal Title(Ko) :정밀농업과학기술
  • Volume : 8
  • No :3
  • Pages :215-239
  • Received Date : 2026-07-20
  • Revised Date : 2026-08-20
  • Accepted Date : 2026-08-20
  • DOI :https://doi.org/10.22765/pastj.20260017
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