Multi-Architecture Deep Learning Framework for Plant Disease Detection and Classification in Smart Agriculture

Abstract

Plant diseases are usually a great threat to agriculture in general, for which prompt diagnosis is very important for efficient management action. Most typical leaf diseases that occur on hundreds of plants show their symptoms in the surface leaves. Traditional laboratory diagnosis is costly, time-consuming, requiring a specialized agronomist to confirm results before technology is used. Continually pestering cassava, the most important food crop to about millions in the globe, are leaf diseases that shrink their yields potential. This research narrates a deep learning-based cassava plant disease diagnosis and its classification via leaf image analysis at multi-resolutions. Specifically, it investigates the performance with respect to the four architectures: EfficientNetB4, InceptionV3, ResNet50, and VGG19 - used to differentiate the leaves of cassava ailments. The dense convolutional neural network architecture has been built employing a good amount of plant leaf images from different regions. The dataset features with very rich interclass and intraclass variations along with very complex and challenging scenarios, to which dense neural network adapts pretty well. The performance of the trained model over various parameters validated through five-fold cross-validation as well as unseen data is pretty impressive. With an average cross-validation accuracy of 94 percent with images with complex backgrounds, experimental results indicate that system classifies different plant leaves.

Plant phenotyping relevance

葉画像から植物病害状態を推定する深層学習手法の開発・検証が中心であり、植物表現型計測に該当する。

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