Deep Learning Based Automated Detection of Diseases from Apple Leaf Images

In Agriculture Sciences, detection of diseases is one of the most challenging tasks. The mis-interpretations of plant diseases often lead to wrong pesticide selection, resulting in damage of crops. Hence, the automatic recognition of the diseases at earlier stages is important as well as economical for better quality and quantity of fruits. Computer aided detection (CAD) has proven as a supportive tool for disease detection and classification, thus allowing the identification of diseases and reducing the rate of degradation of fruit quality. In this research work, a model based on convolutional neural network with 19 convolutional layers has been proposed for effective and accurate classification of Marsonina Coronaria and Apple Scab diseases from apple leaves. For this, a database of 50,000 images has been acquired by collecting images of leaves from apple farms of Himachal Pradesh (H.P) and Uttarakhand (India). An augmentation technique has been performed on the dataset to increase the number of images for increasing the accuracy. The performance analysis of the proposed model has been compared with the new two Convolutional Neural Network (CNN) models having 8 and 9 layers respectively. The proposed model has also been compared with the standard machine learning classifiers like support vector machine, k-Nearest Neighbour, Random Forest and Logistic Regression models. From experimental results, it has been observed that the proposed model has outperformed the other CNN based models and machine learning models with an accuracy of 99.2%.