Enchancing Apple Plant Leaf Disease Detection Performance with Transfer Learning Methods

Abstract

It is very important in agriculture to detect diseases in plants and recovery solutions to produce more crop and to improve efficiency. Enhancements in automated disease detection and analysis can offer significant advantages for taking prompt action, enabling interventions at earlier stages to treat the disease and prevent its spread. This proactive approach could help minimize damage to crop yields. This research is aimed at improving classification performance for apple plant leaf disease detection using transfer learning approaches. The goal is to take necessary precautions for unhealthy apple plants for productive agriculture and healthy food. It discriminates sick apple plants from healthy counterparts by implementing image processing with apple leaf photographs. In this study, traditional machine learning methods are applied for apple plant disease detection task and the classification achievement scores are maximized with transfer learning techniques. The experiments are conducted on a real-world data set including 3164 apple leaf images. As a result, those experiments reveal that transfer learning methods especially EfficientNetB0 has made a significant improvement on classification accuracy for this task. Accuracy and F-score values obtained by transfer learning methods are over 99% which states that they can be considered reliable for plant disease detection tasks.

Code and data availability

The paper uses the third-party 'Battal's Leaf Disease Images dataset' (3164 apple leaf images) but provides no public URL, DOI, or deposit identifier for it, and contains no code, model checkpoint, or supplement availability statement with an authors' public link. No paper-specific, actionable public asset is evidenced

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