Plant Leaf Disease Predictor Using Deep Learning

Abstract

Abstract: Artificial intelligence includes deep learning as a subset. Due to its advantages, autonomous learning and feature extraction have been hotly debated in academic and industrial circles in recent years. Image and video processing, voice processing, and natural language processing have all benefited from it. Simultaneously, it has grown into a centre for agricultural plant protection research, which includes, among other things, plant disease recognition and insect range evaluation. Deep learning can assist avoid the drawbacks of artificially selecting disease spot features, improve the objectivity of plant disease feature extraction, and accelerate research and technological change. In this review, we look at how deep learning technology has progressed in the field of agricultural leaf disease detection in recent years. The current trends and challenges in using deep learning and sophisticated imaging techniques to detect plant leaf disease are discussed in this paper. Our findings are expected to be valuable to researchers interested in detecting plant diseases and insect pests. We also discussed some of the current problems and issues that need to be addressed. Keywords: Analysis, Deep Learning, Prediction, Plant Leaf, Resnet 50

Plant phenotyping relevance

植物葉の病害を画像から検出する深層学習・画像処理手法の進展、動向、課題を扱う方法論レビューであり、植物病害状態の表現型取得が中心です。

Code and data availability

The paper states it uses the publicly available PlantVillage dataset (38 classes) downloaded from Kaggle, but provides no authors' URL, deposit, or code availability statement. No author-generated dataset, code, model, or supplement with an actionable public link is present, and no allowed URL corresponds to the asset.

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