Early Detection of Casava Plant Leaf Diseases using EfficientNet-B0

Abstract

Image recognition plays a major role in everyday life applications like medical image analysis, gaming, surveillance and security, industrial automation, and more recently it has gained massive backing in the agricultural industry to identify plant diseases in crops. Plant diseases are a huge problem in agriculture and incorporating machine learning algorithms for their early detection will help better yields and save the farmers from loses. This paper entails the use of such machine learning algorithms to detect leaf diseases in the cassava plant. Cassava is one of the largest sources of carbohydrates for the continent of Africa. It is also very vulnerable to several plant diseases; this in turn threatens the food security of the continent. The present study is based on four of such diseases that affect the cassava yield namely, Cassava Bacterial Blight (CBB), Cassava Brown Streak Disease (CBSD), Cassava Mosaic Disease (CMD), and Cassava Green Mottle (CGM). In this research work, EfficientNet-B0 is proposed for the early detection of these diseases. The EfficientNet-B0 models outperform existing CNNs in terms of accuracy and efficiency while reducing parameter size and FLOPS by an order of magnitude. It is easier to detect disease at an early stage without the assistance of professionals, saving farmers both time and money. And our proposed model gave an accuracy of 92.6%.

Plant phenotyping relevance

キャッサバ葉の画像から病害状態を推定するEfficientNet-B0モデルを開発・評価しており、植物病害表現型の画像ベース推定が中心的な方法論的貢献である。

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