MULTI-SPECTRAL EMBEDDED VISION SYSTEM FOR EARLY PLANT DISEASE DETECTION IN PRECISION FARMING

T. Dinesh; Raguvaran

doi:10.65379/tpsn2013/ijaemsv01i02p4

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MULTI-SPECTRAL EMBEDDED VISION SYSTEM FOR EARLY PLANT DISEASE DETECTION IN PRECISION FARMING

Author(s): T. Dinesh, Raguvaran

Volume RegistryVolume 1

Issue PeriodIssue 02

Published Date30 Aug 2025

DOI Identifier🔗 10.65379/tpsn2013/ijaemsv01i02p4

Abstract

Agriculture depends on detecting plant disease early and using pesticides efficiently. Traditional methods, such as manual inspection and spraying large areas, are slow, harmful, and environmentally wasteful. These systems often lack real-time response and precise targeting. This project introduces a multi-spectral embedded vision system designed to revolutionis how plant diseases are detected and treated in precision farming. The traditional methods rely on human inspection and blanket pesticide spraying. This system replaces this with an intelligent and automated solution. By using a RetineXNet to enhance lighting conditions and DnCNN to clean noisy images and the system ensures high-quality input data for disease classification. These AI models run in Python and communicate with an ESP32 microcontroller. Which controls a relay-driven pesticide pump, displays the disease type on an LCD and triggers an alert. They are all powered by a 12V battery and making it fully field deployable. The innovation lies in combining multi-spectral imaging with real-time embedded AI. To enable early detection and targeted pesticide application. This not only improves accuracy but also drastically reduces chemical usage and making farming smarter, cleaner and more sustainable.

Keywords

Multi-spectral imaging Embedded vision system Plant disease detection RetinexNet DnCNN ESP32 microcontroller precision agriculture AI in smart farming realtime disease classification.

References (18)

L. Li, S. Zhang, and B. Wang, "Plant disease detection and classification by deep learning— A review," IEEE Access, vol. 9, pp. 56683– 56698, 2021, doi: https://doi.org/10.1109/ACCESS.2021.3069646
S. Qazi, B. A. Khawaja, and Q. U. Farooq, "IoT-equipped and AI-enabled next generation smart agriculture: A critical review, current challenges and future trends," IEEE Access, vol. 10, pp. 21219–21235, 2022, doi: https://doi.org/10.1109/ACCESS.2022.3152544
A. S. Ibrahim et al., "AI-IoT-based smart agriculture pivot for plant disease detection and treatment," Scientific Reports, vol. 15, no. 1, pp. 1–16, 2025, doi: https://doi.org/10.1038/s41598-025- 98454-6.
J. P. Nyakuri, C. Nkundineza, O. Gatera, K. Nkurikiyeyezu, and G. Mwitende, "AI and IoT-powered edge device optimised for crop pest and disease detection," Scientific Reports, E-ISSN: 3107-5843 International Journal of Advanced Engineering and Management System Volume 01, Issue 02, July-September 2025, pp. 60 - 74 Volume 01, Issue 02, July-September 2025 vol. 15, no. 1, pp. 1–14, 2025, doi: https://doi.org/10.1038/s41598-025-06452-5
X. Xue, V. Thakur, H. Dhumras, R. H. Jhaveri, and T. R. Gadekallu, "Real-time pest detection using ResNet-50 and vision transformer: An IoT-enabled mobile application for smart agriculture," IEEE Transactions on Consumer Electronics, 2025, doi: https://doi.org/10.1109/TCE.2025.3599533
T. Dinesh, & Asst.Prof. Siva Balan. (2025). Sustainable Crop Yield Forecasting Using Advanced Machine Learning Techniques Based on Comprehensive Analysis of Soil Health Parameters and Environmental Factors. International Journal of Advanced Engineering and Management System, 1(1), 41-52. Doi: https://doi.org/10.65379/tpsn2013/ijaemsv01i01p4
R. Indumathi, N. Saagari, V. Thejuswini, and R. Swarnareka, "Leaf disease detection and fertiliser suggestion," in Proceedings of the 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 2019, pp. 1 –7, doi: https://doi.org/10.1109/ICSCAN.2019.8878781
S. V. Militante, B. D. Gerardo, and N. V. Dionisio, "Plant leaf detection and disease recognition using deep learning," in Proceedings of the 2019 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE), Yunlin, Taiwan, 2019, pp. 579 –582, doi: https://doi.org/10.1109/ECICE47484.2019.8942686
W. Shafik, A. Tufail, A. Namoun, L. C. De Silva, and R. A. A. H. M. Apong, "A systematic literature review on plant disease detection: Motivations, classification techniques, datasets, challenges, and future trends," IEEE Access, vol. 11, pp. 59174– 59203, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3284760
P. Jiang, Y. Chen, B. Liu, D. He, and C. Liang, "Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks," IEEE Access, vol. 7, pp. 59069–59080, 2019, doi: https://doi.org/10.1109/ACCESS.2019.2914929
G. Shrestha, Deepsikha, M. Das, and N. Dey, "Plant disease detection using CNN," in Proceedings of the 2020 IEEE Applied Signal Processing Conference (ASPCON), Kolkata, India, 2020, pp. 109–113, doi: https://doi.org/10.1109/ASPCON49795.2020.9276722
L. S. Puspha Annabel, T. Annapoorani, and P. Deepalakshmi, "Machine learning for plant leaf disease detection and classification – A review," in Proceedings of the 2019 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, 2019, pp. 0538 –0542, doi: https://doi.org/10.1109/ICCSP.2019.8698004
Y. Tang, C. Chen, A. C. Leite, and Y. Xiong, "Editorial: Precision control technology and application in agricultural pest and disease control," Frontiers in Plant Science, vol. 14, Art. no. 1163839, 2023, doi: https://doi.org/10.3389/fpls.2023.1163839
A. K. Mahlein, "Plant disease detection by imaging sensors—parallels and specific demands for precision agriculture and plant phenotyping," Plant Disease, vol. 100, no. 2, pp. 241–251, 2016, doi: https://doi.org/10.1094/PDIS- 03-15-0340-FE.
M. K. Rajagopal and B. M. MS, "Artificial intelligence-based drone for early disease detection and precision pesticide management in cashew farming," arXiv preprint arXiv:2303.08556, 2023.
R. Chin, C. Catal, and A. Kassahun, "Plant disease detection using drones in precision agriculture," Precision Agriculture, vol. 24, no. 5, pp. 1663–1682, 2023, doi: https://doi.org/10.1007/s11119-023-10014-y
A. K. Mahlein, E. C. Oerke, U. Steiner, and H. W. Dehne, "Recent advances in sensing plant diseases for precision crop protection," European Journal of Plant Pathology, vol. 133, no. 1, pp. 197–209, 2012, doi: https://doi.org/10.1007/s10658-011-9878-z
P. Kulkarni, A. Karwande, T. Kolhe, and S. Kamble, "Plant disease detection using image processing and machine learning," International Journal of Advanced Research in Computer and Communication Engineering, vol. 10, no. 5, pp. 112–118, 2021.

T. Dinesh and Raguvaran, "MULTI-SPECTRAL EMBEDDED VISION SYSTEM FOR EARLY PLANT DISEASE DETECTION IN PRECISION FARMING," Int. J. Adv. Eng. Manag. Syst., vol. 1, no. 2, pp. 120-133, 2025. doi: 10.65379/tpsn2013/ijaemsv01i02p4.

MULTI-SPECTRAL EMBEDDED VISION SYSTEM FOR EARLY PLANT DISEASE DETECTION IN PRECISION FARMING

Abstract

Keywords

References (18)

Format Citation Record