A refined DenseNet Deep Learning Network for Apple Leaf Disease Prediction

Abstract

Revolutionary strategies for managing agricultural diseases have been made possible by recent developments in machine learning, especially in apple leaf disease prediction. According to recent research, convolutional neural networks with fewer connections between layers near their inputs and those near the output can be trained with far greater depth, accuracy, and efficiency. To make a more precise diagnosis of apple-leaf defects than existing architectures, this work proposed a method combining DenseNet-121 and optimising transfer learning strategy for multiclass classification. DenseNet-121 is used as a feature extractor as it strengthens feature propagation and reuse, leading to sustainable feature parameter reduction. The experiment is performed on 3 publicly accessible datasets with 3 classes, 6 classes and 9 classes of apple disease in leaf. The network architecture is fed with augmented data to avoid the problem of class imbalance. The proposed model has responded exceptionally well on all three datasets, claiming 99.9%, 99% and 96% accuracy. Comparative studies and experimental data demonstrate the competitive prediction accuracy of the suggested approach.

Code and data availability

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