Detecting Plant Diseases Using Machine Learning Models

Nazar Kohut, Oleh Basystiuk, Nataliya Shakhovska and Nataliia Melnykova ()
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Nazar Kohut: Department of Artificial Intelligence, Lviv Polytechnic National University, 79013 Lviv, Ukraine
Oleh Basystiuk: Department of Artificial Intelligence, Lviv Polytechnic National University, 79013 Lviv, Ukraine
Nataliya Shakhovska: Department of Artificial Intelligence, Lviv Polytechnic National University, 79013 Lviv, Ukraine
Nataliia Melnykova: Department of Artificial Intelligence, Lviv Polytechnic National University, 79013 Lviv, Ukraine

Sustainability, 2024, vol. 17, issue 1, 1-19

Abstract: Sustainable agriculture is pivotal to global food security and economic stability, with plant disease detection being a key challenge to ensuring healthy crop production. The early and accurate identification of plant diseases can significantly enhance agricultural practices, minimize crop losses, and reduce the environmental impacts. This paper presents an innovative approach to sustainable development by leveraging machine learning models to detect plant diseases, focusing on tomato crops—a vital and globally significant agricultural product. Advanced object detection models including YOLOv8 (minor and nano variants), Roboflow 3.0 (Fast), EfficientDetV2 (with EfficientNetB0 backbone), and Faster R-CNN (with ResNet50 backbone) were evaluated for their precision, efficiency, and suitability for mobile and field applications. YOLOv8 nano emerged as the optimal choice, offering a mean average precision (MAP) of 98.6% with minimal computational requirements, facilitating its integration into mobile applications for real-time support to farmers. This research underscores the potential of machine learning in advancing sustainable agriculture and highlights future opportunities to integrate these models with drone technology, Internet of Things (IoT)-based irrigation, and disease management systems. Expanding datasets and exploring alternative models could enhance this technology’s efficacy and adaptability to diverse agricultural contexts.

Keywords: object detection; computer vision; YOLO; YOLOv8; EfficientDet; Faster R-CNN; CNN; agriculture; diseases (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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