Tomato Plant Disease Identification

Abstract: Plant infections are one of the issues in the agricultural industry. Disease on plant leads to the substantial drop in both the quality and output of agricultural goods. Early identification and detection of plant diseases are therefore crucial. Plant illnesses frequently manifest on the leaves, and the diseased leaves' characteristics might vary and make them difficult to identify. Automatic disease identification is therefore challenging. It is possible to employ image processing techniques. Typically, illness symptoms can be noticed on the leaves. In this work Convolution neural networks and ensemble classifiers are employed in this study to categorise tomato disease into 7 groups (six disease and one healthy class), each with 100 photos. This study has effectively identified tomato plant disease using an automatic leaf image detection method with an accuracy of 96% and 92%.

Keywords: Image processing,Convolutional neural network, Ensemble