Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4

Deep learning has made essential contributions to classification and detection tasks applied to precision agriculture; however, it is vitally important to move towards an adoption of these techniques and algorithms through low-cost and low-consumption devices for daily use in crop fields. In this paper, we present the training and evaluation of four recent Convolutional Neural Networks models for the classification of diseases in tomato leaves. A subset of the Plantvillage dataset consisting of 18,160 RGB images has been divided into ten classes for transfer learning. The selected models have depthwise separable convolution architecture for application in low-power devices. Evaluation and analysis quantitatively and qualitatively is performed via quality metrics and saliency maps. Finally, an implementation on the Raspberry Pi 4 microcomputer with a graphical user interface is developed.

Automated summary

This study focuses on applying deep learning techniques to disease classification in tomato leaves using low-cost and low-power devices, training and evaluating convolutional neural network models suitable for such devices. Quantitative and qualitative evaluation and analysis are conducted through quality metrics and saliency maps, culminating in an implementation on Raspberry Pi 4 with a graphical user interface.