Plant Disease Detection using Deep Learning in Banana and Sunflower

Abstract

In recent years plant disease detection and classification is finding a lot of scope in the field of agriculture. The use of image pre-processing along with deep learning techniques is making the role of farmers easy in the process of plant leaf disease detection. In this paper we propose a deep learning technique, ResNet-50 for the identification and classification of leaf diseases mainly in banana and sunflower. Images for the training and testing purpose are collected by visiting the farms and from village dataset for normal, leaf spot, leaf blight, powdery mildew, bunchy top, sigatoka, panama wilt. Pre-processing is done to remove eliminate the noise in the image by converting the RGB input to HSV image. Binary pictures are retrieved to separate the diseased and unaffected portions based on the hue and saturation components. A clustering method is utilized to separate the diseased region from the normal portion and the background. Classification of the disease is carried out using ResNet-50 algorithm. The experimental results obtained are compared with CNN, machine learning algorithms like SVM, KNN, DT and Ensemble algorithm like RF and XG booster. The proposed algorithm provided maximum efficiency compared to other algorithms.

Plant phenotyping relevance

植物葉の画像から病変領域を抽出し、深層学習で病害を分類する手法が研究の中心であり、植物の病害状態を直接推定するため対象範囲に含める。

Code and data availability

The paper describes a banana/sunflower leaf-disease image dataset (~200 images collected from farms and the 'village dataset') and ResNet-50 analysis, but contains no public deposit, availability statement, or authors' URL for the images, dataset, code, or trained model. The 'village dataset' reference is a generic, un

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