Disease diagnosis in Cassava leaves using CNN design and ResNet algorithm

Abstract

Abstract Growth rate of crops is significantly lowered by illnesses that affect plants. It is impossible for anyone to eat the crops since they are tainted with various diseases. Farmers may suffer enormous losses as a consequence. Since cassava is an important food source in several countries, the financial system might be seriously damaged by the issue at hand. Traditional plant pathogen detection is labour-intensive and error-prone. It is not typically a dependable strategy to identify and stop the spread of plant viruses. Innovative technologies like deep learning as well as machine learning might aid in the early detection of plant diseases as an approach to get around these problems. The main goal of the work is to employ deep learning to image classification in order to accurately identify diseases that especially impact cassava plants. This recognition may make it possible to implement preventative measures like the specific application of chemical pesticides or confinement of contaminated crops. Each and every training and testing image comes from a rural area in the natural world. Using a specific collection of information, the simulation has been verified to ascertain its true results. The installation of a precise disease identification and mitigation model has the potential to significantly increase the durability of the cassava crop, improving food production and the quality of life for millions of people who are dependent upon this valuable crop.

Plant phenotyping relevance

カッサバ葉の画像から病害状態をCNN/ResNetで分類する手法が研究の中心であり、植物の病害表現型を直接推定しているため含める。

Code and data availability

The paper uses the public Kaggle Cassava Leaf Disease Dataset for training/testing its ResNet-50 cassava disease classifier, but no authors' code, trained model, or paper-specific data deposit is provided. The Data Availability statement only offers datasets 'upon reasonable request'. The only allowed URL is the paper'

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