Smart Farm Rover: Autonomous Plant Disease Detection and Classification with Machine Learning

Arun Madakannu; R. K. V. Giridhesh; Sanjith Chandran

doi:10.62762/JCOR.2025.626841

CiteScore

Impact Factor

Volume 1, Issue 1, Journal of Computational Optimization and Reasoning

Volume 1, Issue 1, 2025

Submit Manuscript Edit a Special Issue

Article QR Code

Scan the QR code for reading

Popular articles

Research on A Ship Trajectory Classification Method Based on Deep Learning Bridging Modalities: A Survey of Cross-Modal Image-Text Retrieval A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data Visual Feature Extraction and Tracking Method Based on Corner Flow Detection Inaugural Editorial of the Chinese Journal of Information Fusion YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems Short and Long-Term Renewable Electricity Demand Forecasting Based on CNN-Bi-GRU Model Simultaneous Spatiotemporal Bias Compensation and Data Fusion for Asynchronous Multisensor Systems

Journal of Computational Optimization and Reasoning, Volume 1, Issue 1, 2025: 3-10

Open Access | Research Article | 26 July 2025

Smart Farm Rover: Autonomous Plant Disease Detection and Classification with Machine Learning

Arun Madakannu 1 *

R. K. V. Giridhesh 1

Sanjith Chandran 1

1 Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu 626005, India

* Corresponding Author: Arun Madakannu, [email protected]

DOI: 10.62762/JCOR.2025.626841

Received: 22 January 2025, Accepted: 29 June 2025, Published: 26 July 2025

PDF (4.91 MB)

Article Metrics Cite This Article

Abstract

Agriculture plays a major role in India’s growth. Timely prediction of diseases will minimize their impact and improve productivity in agriculture. This work focuses on designing a rover-based system for automated plant disease detection. The rover can be utilized to detect plants without human intervention. The COCO/SSD model is used for object detection. After detecting the plant, the motor is actuated to move the rover near it. The “Plant Village” dataset is used to train the model. It consists of various images of healthy and diseased plants. In this project, we have used tomato, potato, corn, and grape images to train the model for disease identification. We have used various deep learning algorithms such as VGG16, VGG19, and AlexNet. Among these, AlexNet achieved the highest testing accuracy of 95.72%. The AlexNet model is used to detect plant leaf diseases. Using Python, the model detects the plant leaf disease and notifies the user. Simple Mail Transfer Protocol (SMTP) is used to send the captured image and the predicted disease to the user. The ESP32-CAM and Raspberry Pi were used to perform plant detection and disease identification. Thus, identifying diseases with the help of a rover will increase productivity and benefit farmers.

Graphical Abstract

Smart Farm Rover: Autonomous Plant Disease Detection and Classification with Machine Learning

Keywords

CNN

AlexNet

plant disease

rover

farming

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate

Not applicable.

References

Hassan, S. M., & Maji, A. K. (2022). Plant disease identification using a novel convolutional neural network. IEEE Access, 10, 5390-5401.
[CrossRef] [Google Scholar]
Zinonos, Z., Gkelios, S., Khalifeh, A. F., Hadjimitsis, D. G., Boutalis, Y. S., & Chatzichristofis, S. A. (2022). Grape leaf diseases identification system using convolutional neural networks and LoRa technology. IEEE Access, 10, 122-133.
[CrossRef] [Google Scholar]
Zhou, C., Zhou, S., Xing, J., & Song, J. (2021). Tomato leaf disease identification by restructured deep residual dense network. IEEE Access, 9, 28822-28831.
[CrossRef] [Google Scholar]
Zhang, X., Qiao, Y., Meng, F., Fan, C., & Zhang, M. (2018). Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access, 6, 30370-30377.
[CrossRef] [Google Scholar]
Rashid, M., Bari, B. S., Yusup, Y., Kamaruddin, M. A., & Khan, N. (2021). A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction. IEEE Access, 9, 63406-63439.
[CrossRef] [Google Scholar]
Levoir, S. J., Farley, P. A., Sun, T., & Xu, C. (2020). High-accuracy adaptive low-cost location sensing subsystems for autonomous rover in precision agriculture. IEEE Open Journal of Industry Applications, 1, 74-94.
[CrossRef] [Google Scholar]
Liu, Z., Bashir, R. N., Iqbal, S., Shahid, M. M. A., Tausif, M., & Umer, Q. (2022). Internet of Things (IoT) and machine learning model of plant disease prediction–blister blight for tea plant. IEEE Access, 10, 44934-44944.
[CrossRef] [Google Scholar]
Materne, N., & Inoue, M. (2018). IoT monitoring system for early detection of agricultural pests and diseases. Proceedings of the 12th South East Asian Technical University Consortium (SEATUC).
[CrossRef] [Google Scholar]
Sharma, P., Hans, P., & Gupta, S. C. (2020). Classification of plant leaf diseases using machine learning and image preprocessing techniques. 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 480-484.
[CrossRef] [Google Scholar]
Lv, M., Zhou, G., He, M., Chen, A., Zhang, W., & Hu, Y. (2020). Maize leaf disease identification based on feature enhancement and DMS-Robust AlexNet. IEEE Access, 8, 57952-57966.
[CrossRef] [Google Scholar]
Waheed, A., Goyal, M., Gupta, D., Khanna, A., Hassanien, A. E., & Pandey, H. M. (2020). An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Computers and Electronics in Agriculture, 175, 105456.
[CrossRef] [Google Scholar]
Ahila Priyadharshini, R., Arivazhagan, S., Arun, M., & Mirnalini, A. (2019). Maize leaf disease classification using deep convolutional neural networks. Neural Computing and Applications, 31(12), 8887-8895.
[CrossRef] [Google Scholar]
Priyadharshini, R. A., Arivazhagan, S., & Arun, M. (2023). A comparative study of traditional machine learning and deep learning approaches for plant leaf disease classification. Applied Intelligence in Human-Computer Interaction, 47-65.
[CrossRef] [Google Scholar]
Pawar, S., Shedge, S., Panigrahi, N., Jyoti, A. P., Thorave, P., & Sayyad, S. (2022). Leaf disease detection of multiple plants using deep learning. 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON), 241-245.
[CrossRef] [Google Scholar]
T. S. M., S. K. S., S. M. S., & Devi, P. R. (2021). Leaf disease detection using deep learning. 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), 1797-1804.
[CrossRef] [Google Scholar]

Cite This Article

APA Style

Madakannu, A., Giridhesh, R. K. V., & Chandran, S. (2025). Smart Farm Rover: Autonomous Plant Disease Detection and Classification with Machine Learning. Journal of Computational Optimization and Reasoning, 1(1), 3–10. https://doi.org/10.62762/JCOR.2025.626841

Article Metrics

Citations:

Google Scholar

Crossref

Scopus

Web of Science

Article Access Statistics:

PDF Downloads: 8

Publisher's Note

ICCK stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and Permissions

Copyright © 2025 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

Journal of Computational Optimization and Reasoning

ISSN: request pending (Online) | ISSN: request pending (Print)

Email: [email protected]

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/icck/

Google Scholar

Crossref

Scopus

Web of Science

We use cookies