Deep Learning model of sequential image classifier for crop disease detection in plantain tree cultivation

Abstract

Plantain tree is the most popular crop grown all over the world and banana (Musa spp.) is the most marketable fruit. It is the leading food in many countries, especially in developing countries. Plant diseases are significant aspects that result in a serious reduction in the quantity and quality of fruit crops. Plantain tree cultivation is affected by various diseases such as Black Sigatoka/Yellow sigatoka, Panama, Bunchy top, Moko, chlorosis, etc. Rapid and novel approaches for the apt discovery of diseases help farmers in developing better decisions and efficient control measures. Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN) have been proved their efficiency in several fields and it has recently moved in the field of crop disease classification and detection. The objective of this research work is to create a Deep Learning Model for the disease classification and its early prediction to support farmers in plantain tree cultivation. A new sequential image classification model is proposed to detect the diseases by combining RNN and CNN, which is named as Gated-Recurrent Convolutional Neural Network (G-RecConNN). The input to the proposed model is the sequences of plant images. The experiments are carried out in real-time datasets collected from the state named Tamil Nadu situated in the Southern part of India. This method aims at numerous advantages such as reduced pre-processing of the data, easy online performance evaluation and advancements with less real data, etc. The experimental results inspired the utilization of the G-RecConNN model with farmer support systems that will process continuous banana tree images as part or whole for the early detection of banana tree diseases.

Plant phenotyping relevance

バナナ葉・樹体画像から病害状態を推定する深層学習画像分類法の開発が研究の中心であり、植物病害フェノタイピングに該当する。

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