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Agronomy Science Baner text

Vol. 80 No. 4 (2025)

Articles

Advancements in plant protection - the application of machine learning to the detection of maize infestations

DOI: https://doi.org/10.24326/as.2025.5569
Submitted: July 9, 2025
Published: 31.12.2025

Abstract

Plant infestations cause significant economic losses in agriculture, necessitating rapid and accurate detection for optimized agrotechnical operations and reduced environmental pollution. This study addresses this challenge by proposing a customized convolutional neural network (CNN) architecture for detecting corn leaf worm infestations in maize. The research focuses on developing unique CNN models through extensive experimentation, systematically adjusting hyperparameters like optimizers, filter numbers, and kernel sizes. The study’s main contributions include the design of an accurate CNN classifier, and its implementation in a user-friendly smartphone application. The research highlights the importance of hyperparameter tuning in CNN performance, demonstrating that optimal configurations lead to high accuracy (up to 95% for accuracy, precision, recall, specificity, and
F1-score). While the current model focuses on a single pest, the findings underscore the potential of custom CNN classifiers in vision systems for automated crop inspection, offering a promising solution for minimizing crop losses and the environmental impact of chemical plant protection products.

References

  1. REFERENCES
  2. Acharya R., 2020. Corn Leaf Infection Dataset, https://www.kaggle.com/datasets/qramkrishna/ corn-leaf-infection-dataset [access: 04.15.2023].
  3. Babendreier D., Toepfer S., Bateman M. et al., 2022. Potential management options for the invasive moth spodoptera frugiperda in Europe. J. Econ. Entomol. 115(6), 1772–1782. https://doi.org/ 10.1093/JEE/TOAC089
  4. Baldi P., Brunak S., Chauvin Y. et al., 2000. Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16(5), 412–424. https://doi.org/10.1093/ BIOIN-FORMATICS/16.5.412
  5. Barbedo J.G.A., 2018. Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Comput. Electron. Agric. 153, 46–53. https://doi.org/10.1016/J.COMPAG.2018.08.013
  6. Berka A., Hafiane A., Es-Saady Y. et al., 2023. CactiViT: Image-based smartphone application and transformer network for diagnosis of cactus cochineal. Artif. Intell. Agric. 9, 12–21. https://doi.org/10.1016/J.AIIA.2023.07.002
  7. Chicco D., Jurman G., 2020. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21(1), 1–13. https://doi.org/10.1186/s12864-019-6413-7
  8. Coviello L., Cristoforetti M., Jurman G. et al., 2020. GBCNet: In-field grape berries counting for yield estimation by dilated CNNs. Appl. Sci. 10(14), 4870. https://doi.org/10.3390/ APP10144870
  9. Duan K., Keerthi S.S., Chu W. et al., 2003. Multi-category classification by soft-max combination of binary classifiers. In: T. Windeatt, F. Roli (eds), Multiple classifier systems. MCS 2003. Springer–Berlin–Heidelberg. https://doi.org/10.1007/3-540-44938-8_13
  10. Ebrahimi M.A., Khoshtaghaza M.H., Minaei S. et al., 2017. Vision-based pest detection based on SVM classification method. Comput. Electron. Agric. 137, 52–58. https://doi.org/10.1016/ J.COMPAG.2017.03.016
  11. FAOSTAT, 2024. https://www.fao.org/faostat/en/#data/QV [access: 23.10.2024].
  12. Ferentinos K.P., 2018. Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318. https://doi.org/10.1016/J.COMPAG.2018.01.009
  13. Gao J., French A.P., Pound M.P. et al., 2020. Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields. Plant Methods 16(1), 1–12. https://doi.org/ 10.1186/s13007-020-00570-z
  14. Gill H.S., Khalaf O.I., Alotaibi Y. et al., 2022. Multi-model CNN-RNN-LSTM based fruit recogni-tion and classification. Intell. Autom. Soft Comput. 33(1), 637–650. https://doi.org/10.32604/ IASC.2022.022589
  15. Hasan A.S.M.M., Sohel F., Diepeveen D. et al., 2021. A survey of deep learning techniques for weed detection from images. Comput. Electron. Agric. 184, 106067. https://doi.org/10.1016/ J.COMPAG.2021.106067
  16. Hughes D.P., Salathe M., 2015. An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv, 1511.08060. https://doi.org/10.48550/ arXiv.1511.08060
  17. Jeger M., Bragard C., Caffier D. et al., 2018. Pest risk assessment of Spodoptera frugiperda for the European Union. EFSA J. 16(8). https://doi.org/10.2903/J.EFSA.2018.5351
  18. Jia W., Tian Y., Luo R. et al., 2020. Detection and segmentation of overlapped fruits based on opti-mized mask R-CNN application in apple harvesting robot. Comput. Electron. Agric. 172, 105380. https://doi.org/10.1016/J.COMPAG.2020.105380
  19. Jiao L., Dong S., Zhang S. et al., 2020. AF-RCNN: An anchor-free convolutional neural network for multi-categories agricultural pest detection. Comput. Electron. Agric. 174, 105522. https://doi.org/10.1016/J.COMPAG.2020.105522
  20. Kamarudin M.H., Ismail Z.H., Saidi N.B., 2021. Deep learning sensor fusion in plant water stress assessment: a comprehensive review. Appl. Sci. 11(4), 1403. https://doi.org/10.3390/ APP11041403
  21. Khan S., Tufail M., Khan M.T. et al., 2021. Deep-learning-based spraying area recognition system for unmanned-aerial-vehicle-based sprayers. Turk. J. Elec. Eng. Comp. Sci. 29(1), 241–256. https://doi.org/10.3906/elk-2004-4
  22. Khanramaki M., Askari Asli-Ardeh E., Kozegar E., 2021. Citrus pests classification using an en-semble of deep learning models. Comput. Electron. Agric. 186, 106192. https://doi.org/ 10.1016/J.COMPAG.2021.106192
  23. Kussul N., Lavreniuk M., Skakun S. et al., 2017. Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci. Remote Sens. Lett. 14(5), 778–782. https://doi.org/10.1109/LGRS.2017.2681128
  24. Lanjewar M.G., Panchbhai K.G., 2023. Convolutional neural network based tea leaf disease predic-tion system on smart phone using paas cloud. Neural Comput. App. 35(3), 2755–2771. https://doi.org/10.1007/S00521-022-07743-y
  25. Lanjewar M.G., Parab J.S., 2024. CNN and transfer learning methods with augmentation for citrus leaf diseases detection using PaaS cloud on mobile. Multimed. Tools App. 83(11), 31733–31758. https://doi.org/10.1007/S11042-023-16886-6
  26. Li Y., Wang H., Dang L.M. et al., 2020. Crop pest recognition in natural scenes using convolutional neural networks. Comput. Electron. Agric. 169, 105174. https://doi.org/10.1016/J.COMPAG.2019.105174
  27. Loyani L., Machuve D., 2021. A deep learning-based mobile application for segmenting tuta absolu-ta’s damage on tomato plants. Eng. Technol. Appl. Sci. Res. 11(5), 7730–7737. https://doi.org/10.48084/ETASR.4355
  28. Mallick M.T., Biswas S., Das A.K. et al., 2023. Deep learning based automated disease detection and pest classification in Indian mung bean. Multimed. Tools App. 82(8), 12017–12041. https://doi.org/10.1007/S11042-022-13673-7
  29. Mohan K.J., Balasubramanian M., Palanivel S., 2016. Detection and recognition of diseases from paddy plant leaf images. Int. J. Comput. App. 144(12), 975–8887.
  30. Mohanty S.P., Hughes D.P., Salathé M., 2016. Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 215232. https://doi.org/10.3389/FPLS.2016.01419
  31. Bosco de Oliveira A. (ed.), 2019. Abiotic and biotic stress in plants. IntechOpen. https://doi.org/10.5772/intechopen.77845
  32. Peyal H.I., Nahiduzzaman M., Pramanik M.A.H. et al., 2023. Plant disease classifier: detection of dual-crop diseases using lightweight 2D CNN architecture. IEEE Access 11, 110627–110643. https://doi.org/10.1109/ACCESS.2023.3320686
  33. Qureshi S.H., Khan D.M., Razzaq A. et al., 2024. Comparison of conventional and computer-based detection of severity scales of stalk rot disease in maize. Sabrao J. Breed. Genet. 56(1), 292–301. https://doi.org/10.54910/SABRAO2024.56.1.26
  34. Srivastava A.K., Safaei N., Khaki S. et al., 2022. Winter wheat yield prediction using convolutional neural networks from environmental and phenological data. Sci. Rep. 12(1), 1–14. https://doi.org/10.1038/s41598-022-06249-w
  35. Storey G., Meng Q., Li B., 2022. Leaf disease segmentation and detection in apple orchards for precise smart spraying in sustainable agriculture. Sustainability 14(3), 1458. https://doi.org/ 10.3390/SU14031458
  36. Thenmozhi K., Srinivasulu Reddy U., 2019. Crop pest classification based on deep convolutional neural network and transfer learning. Comput. Electron. Agric. 164, 104906. https://doi.org/ 10.1016/J.COMPAG.2019.104906
  37. van den Berg J., Britz C., du Plessis H., 2021. Maize Yield response to chemical control of Spodop-tera frugiperda at different plant growth stages in South Africa. Agriculture 11(9), 826. https://doi.org/10.3390/AGRICULTURE11090826
  38. Vujovic Z., 2021. Classification model evaluation metrics. Int. J. Adv. Comput. Sci. Appl. 12(6). https://doi.org/10.14569/IJACSA.2021.0120670
  39. Wang J., Li Y., Feng H. et al., 2020. Common pests image recognition based on deep convolutional neural network. Comput. Electron. Agric. 179, 105834. https://doi.org/10.1016/J.COMPAG.2020.105834
  40. Xu W., Sun L., Zhen C. et al., 2022. Deep learning-based image recognition of agricultural pests. Appl. Sci. 12(24), 12896. https://doi.org/10.3390/APP122412896
  41. Yang C., Teng Z., Dong C. et al., 2022. In-field citrus disease classification via convolutional neural network from smartphone images. Agriculture 12(9), 1487. https://doi.org/10.3390/ AGRI-CULTURE12091487

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