Redefining Agricultural Disease Management using AI and ML

Precision agriculture is a fast growing field that may contemporary issues surrounding agricultural sustainability are increasingly being addressed through innovative technologies. Machine learning, a cutting-edge tool that enhances precision agriculture, enables sophisticated methods for detecting...

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Vydáno v:	2025 International Conference on Intelligent Computing and Control Systems (ICICCS) s. 720 - 725
Hlavní autoři:	B, Venkata Srinivasulu, Dixith, K.Sai, Shivaram, Keerthi, Reddy, Ch.Karthik
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 19.03.2025
Témata:	Advanced detection methods Agricultural technology Bounding box-based studies Cameras Classification algorithms Computational efficiency Computational study Datasets for plant diseases Deep learning Image classification Machine learning Machine learning algorithms MobileNetv2 Object detection algorithms Object recognition Plant disease classification Plant disease detection Plant diseases PlantDoc dataset Precision agriculture Real-time systems ResNet50 Sustainability Sustainable development Video-based studies YOLOv5
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Popis
Shrnutí:	Precision agriculture is a fast growing field that may contemporary issues surrounding agricultural sustainability are increasingly being addressed through innovative technologies. Machine learning, a cutting-edge tool that enhances precision agriculture, enables sophisticated methods for detecting and classifying plant diseases. This paper offers a comprehensive review of how ML and DL techniques are applied in precision agriculture, focusing on disease identification and categorization. A novel classification framework is introduced, organizing relevant studies into distinct categories-Method-wise Classification Based Approaches. First, we categorize the studies method wise into classification (e.,g, image, and video-based studies) or object detection (e. g., bounding box-based studies) algorithms. Moreover, we provide existing archives for the classification and tracking of plant infections. By presenting a systematic framework that groups machine learning techniques according to their methodology, this study seeks to close the gap between the current plant disease classification techniques. We offer a comparative study to identify the best methods for disease identification by assessing the effectiveness of cutting-edge classification and object recognition algorithms on the PlantDoc dataset. In addition to streamlining precision agriculture research, the suggested architecture provides a basis for creating plant disease detection models that are more precise and scalable.
DOI:	10.1109/ICICCS65191.2025.10984904