Banana Plant Disease Classification Using Hybrid Convolutional Neural Network.

Abstract

Banana cultivation is one of the main agricultural elements in India, while the common problem of cultivation is that the crop has been influenced by several diseases, while the pest indications have been needed for discovering the infections initially for avoiding the financial loss to the farmers. This problem will affect the entire banana productivity and directly affects the economy of the country. A hybrid convolution neural network (CNN) enabled banana disease detection, and the classification is proposed to overcome these issues guide the farmers through enabling fertilizers that have to be utilized for avoiding the disease in the initial stages, and the proposed technique shows 99% of accuracy that is compared with the related deep learning techniques.

Plant phenotyping relevance

バナナ葉など植物の観察画像から病害状態を分類するCNN手法を中心に開発・評価しており、植物病害表現型の推定に該当する。

Code and data availability

The paper's ~3500 banana disease images and hybrid CNN/FSVM analysis are not publicly deposited; the authors state data will be made available upon request, and no public code or dataset URL is provided.

No evidence-backed public reproduction asset is currently recorded.