Abstract
The apple is undoubtedly the most popular fruit on the planet. Climatic conditions, soil quality, and efficient orchard management are essential factors that directly affect fruit harvest and apple agriculture, just as they do in every other commercial farming. Unfortunately, apple orchards are under incessant peril from umpteen fungal, bacterial, viral pathogens, and insects over the growing season. The present research work aims to detect and identify foliar diseases using images of apple leaves with one or more foliar diseases. The present method of identifying diseases by an expert is time-consuming, costly, and inefficient for large orchards. Our proposed model, which is an ensemble of three pre-trained deep convolutional neural networks, namely, ResNet101V2, Xception, and InceptionResNetV2, attempts to classify apple tree leaves as healthy or infected with one or more of the five disease classes. The dataset is improved and expanded using various data augmentation techniques on the training images. Experimental analysis on the Plant Pathology 2021-FGVC8 dataset shows that our proposed model achieves remarkable precision, recall, and F1-score of 0.9743, 0.9541, and 0.9625, respectively, on the testing dataset. Our proposed model performed well across multiple metrics and can be used to assist farmers in correctly identifying plant health efficiently by overcoming the limitations of existing techniques.
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Kejriwal, S., Patadia, D., Sawant, V. (2022). Apple Leaves Diseases Detection Using Deep Convolutional Neural Networks and Transfer Learning. In: Uddin, M.S., Bansal, J.C. (eds) Computer Vision and Machine Learning in Agriculture, Volume 2. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-9991-7_13
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