Smart Diagnosis: Early Detection and Management of Plant Diseases

Abstract

Plant diseases threaten global food security and cause significant financiallosses in agriculture. Early detection and precise diagnosis are critical for effective disease management. This study explores a novel approach to plant disease identification using the You Only Look Once (YOLOv12) algorithm combined with few-shot learning techniques. By leveraging a limited dataset, the model is trained to classify citrus plant leaf images into four categories: healthy, greening, black spot, and canker. The proposed system enhances disease detection efficiency, enabling farmers to take timely preventive measures. Our approach demonstrates the potential of few-shot learning in agricultural disease diagnosis, reducing the need for extensive labeled datasets while maintaining high accuracy.

Introduction

In India, major plant diseases such as rice blast, bacterial leaf blight, wheat rusts, citrus greening, and Panama disease severely impact staple crops, causing significant yield losses. Climate variability and soil persistence complicate management. New detection methods using molecular diagnostics (PCR, LAMP) and IoT-based digital monitoring are improving early disease identification, but accessibility remains a challenge for many farmers.

Traditional disease identification is difficult and labor-intensive, especially for large farms. Early detection, especially of leaf diseases, is critical for maintaining plant health.

An ideal modern plant disease diagnosis system would leverage few-shot learning (FSL) and computer vision (CV) to enable accurate, rapid identification of diseases from images captured by smartphones or drones, even with limited training data. Integrating environmental data would allow personalized management advice, reducing pesticide use and promoting sustainability. This technology could democratize expert knowledge, benefiting regions with limited agricultural support.

Key innovations include building extensive disease image databases through crowdsourcing, applying transfer and meta-learning for model adaptability, and using semi-supervised learning to improve accuracy with minimal labeled data. Real-time feedback would provide tailored treatment recommendations.

The methodology centers on a convolutional neural network trained on a large labeled source dataset and fine-tuned on limited target data using transfer learning, incorporating semi-supervised and iterative approaches to enhance performance by leveraging pseudo-labeled data selected with high confidence.

Experiments on various domain splits and few-shot learning setups show that semi-supervised methods, especially iterative semi-supervised learning, consistently outperform baseline transfer learning in accuracy, demonstrating the effectiveness of the approach in adapting to diverse crops and disease conditions.

Conclusion

Automatic classification of plant leaf diseases based on a few labeled samples is significant to guarantee the yield and quality with low cost of data. In this work, we proposed the semi-supervised few-shot learning scheme, which can improve the average accuracy of few-shot classification by adaptively selecting the pseudo-labeled samples to help fine-tune the model. Through literature research, to our best knowledge, we carried out the first semi-supervised work in the field of few-shot plant diseases classification. The PlantVillage dataset was divided into three split modes, and extensive comparison experiments were executed to prove the correctness and generalization of proposed methods. Considering all the different domain splits and k-shot, the average improvement by the proposed single semi-supervised method is 2.8%, and that by the iterative semi-supervised method is 4.6%. The study utilized the PlantVillage dataset, a publicly available collection of over 54,000 images of both healthy and diseased plant leaves across 38 different classes, representing 14 species of crops. These images were captured under controlled environmental conditions with uniform backgrounds. While widely used as a benchmark in plant disease classification , the controlled nature of the PlantVillage dataset might limit the generalizability of models trained on it to the more complex and variable conditions found in real-world agricultural fields.

References

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Copyright

Copyright © 2025 Dr K Pavendan, Madhudhanusu K, Ganesh V, Vedhavikram R M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.