Deep Feature Extraction for Detection of Tomato Plant Diseases and Pests based on Leaf Images

Abstract

Plant diseases and pests cause yield and quality losses. It has great importance to detect plant diseases and pests quickly and with high accuracy in terms of preventing yield and quality losses. Plant disease and pest detection performed by plant protection experts through visual observation is a labor-intensive process with a high error rate. Developing effective, fast and highly successful computer-aided disease detection systems has become a necessity in terms of precision agriculture applications. In this study, well-known pre-trained convolutional neural network (CNN) models AlexNet, GoogLeNet and ResNet-50 are used as feature extractors. In addition, a deep learning model that concatenate deep features extracted from 3 CNN models has been proposed. The deep features were used to train the support vector machine classifier. The proposed model was used to classify leaf images of tomato plant diseases and pests, which is a subset of open-access PlantVillage dataset consisting of a total of 18835 images belonging to 10 classes including a healthy one. Accuracy, precision, sensitivity and f-score performance metrics were used with the hold-out validation method in determining model performances. Experimental results show that the detection of tomato plant diseases and pests is possible using concatenated deep features with an overall accuracy rate of 96.99%.