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.

Plant phenotyping relevance

リンゴ葉の病徴を画像から分類・予測する深層学習手法の開発と比較評価が中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として採用する。

Code and data availability

The supplied blocks contain only the title page and abstract. The abstract states the experiments were 'performed on 3 publicly accessible datasets' but does not name the datasets, provide any repository identifiers, or give any author code/model availability statement or URL. No paper-specific public phenotype dataset

No evidence-backed public reproduction asset is currently recorded.