Explainable Transfer Learning for Multi-Crop Plant Disease Identification Under Real-Field Conditions

Abstract

Plant diseases have long been considered a major threat to global food production systems. Therefore, early diagnosis is vital to mitigate the risk of these diseases. This task can be challenging, as the number of harmful diseases is substantial. One technology that has gained widespread interest is artificial intelligence, specifically deep learning, which is used to identify plant diseases using leaf patterns. This paper presents two deep learning models, a custom CNN model and a transfer learning model based on the DenseNet-121 architecture. Experiments were carried out using the PlantCity dataset, which consists of twelve subsets of diverse crop species representing fruits, vegetables, and grains with variations among the subsets, including the number of classes, the subset sizes, class distribution, and visual complexity of disease symptoms. The two models were evaluated using multiple metrics, including accuracy, loss, precision, recall, and F1-score. Explainable AI using the LIME technique was deployed to better interpret the acquired results. Results showed that the developed transfer learning model based on DenseNet-121 had superior performance over the CNN model, with accuracies ranging from 86% to 99% across eleven experimented crops. In order to perform an independent experimental validation for the developed model, future work will focus on constructing a local crop dataset captured from Iraqi fields to evaluate the developed models based on the local environment.

Plant phenotyping relevance

葉画像から植物病害状態を推定する深層学習手法を開発し、複数作物・指標で性能評価しているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper's experiments rely on the third-party public PlantCity leaf-image dataset, but no authors' code, trained models, or paper-specific data deposit is described with an availability statement or URL in the supplied blocks. The only allowed URL is the article DOI itself, so no paper-specific, actionable public URL

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