Early Detection and Classification of Apple Leaf Diseases Using Deep Learning Technique

Abstract

Apple leaf diseases represent a critical challenge for apple crop production, threatening both the quality and quantity of harvests. Leaves are essential to the growth and health of apple trees, as they play a pivotal role in photosynthesis and nutrient transport. When these leaves become infected, the diseases can rapidly spread across the entire tree, leading to significant damage that can impact the yield and the overall health of the orchard. The consequences of such outbreaks can be devastating, with severe economic losses for farmers and potentially reduced marketability of the crops. To mitigate these risks, timely and accurate detection of apple leaf diseases is crucial. Early intervention allows farmers to apply targeted treatments and prevent the spread of infection to other parts of the tree or adjacent trees in the orchard. This research proposes an improved deep learning approach, utilizing the VGG16 model, to detect early signs of apple leaf diseases. By focusing on small spots and widespread lesions, the model can identify symptoms at an early stage, allowing for prompt intervention to prevent further spread. The study employs a dataset from Kaggle and a custom repository called Apple Fungal Diseases Dataset, with the classification results compared to other leading techniques to ensure accuracy. By providing a reliable method for early detection, this research aims to support farmers in managing apple leaf diseases more effectively, ultimately contributing to healthier orchards and improved crop yields.