Survey Paper: Plant Disease Detection using CNN

Abstract

“Agriculture provides employment opportunities for village people on large scale in developing country like India. Most of Indian farmers are adopting manual cultivation due to lagging of technical knowledge. In addition that, Plant leaf disease has been one of the major threats to for plants since long ago because it reduces the crop yield and compromises. plant diseases are studied in the literature, mostly focusing on the biological aspects. They make predictions according to the visible surface of plants and leaves. This paper presents a system that is used to classify and detect plant leaf diseases using machine learning techniques. In our work, we have taken specific types of plants; include tomatoes, pepper, and potatoes, as they are the most common types of plants in the world and in Iraq in particular. Using machine learning algorithms, which comprise procedures like dataset construction, loading images, prepping, segmentation, feature extraction, training a classifier, and classification, it is possible to classify plant diseases. This paper presents a Convolutional Neural Network (CNN) model algorithm based method for Agricultural leaf disease detection and classification. So, the neural networks can capture the colours and textures of lesions specific to respective diseases upon diagnosis

Plant phenotyping relevance

CNNによる植物葉の病害検出・分類手法が論文の中心であり、葉の可視症状を直接推定する植物フェノタイピング手法に該当する。

Code and data availability

This survey paper describes a CNN-based plant disease detection system using PlantVillage images, but provides no public dataset link, code deposit, model checkpoint, or supplement with authors' assets. The PlantVillage dataset is cited prior work, not a paper-specific asset, and no availability statements or URLs are.

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