Classification of Apple Plant Leaf Diseases Using Deep Convolutional Neural Network

Food production is one of the main sources of income and livelihood in the world. Various agricultural products are grown as essential food in the largest areas of World. According to various research, many agriculture sectors face loss due to crop diseases Plant anthologists find new methods for diagnosing plant diseases perfectly and reliably. Classification of crop diseases is done by using machine learning techniques. Now a day largest no of plant diseases is identified by using deep learning methods. In this work, we use an effective approach that is based on Otsu's thresholding method in preprocessing whereas plant diseases classify by using deep CNN. In this research, we use a benchmark dataset of apple leafs from Kaggle. The method that we use in this research is not applied to apple plants in previous studies. The dataset contains three classes first we apply preprocessing on the dataset which applies three models on these images named 19 layer CNN, AlexNet, and Inception V3 model. Apple dataset preprocesses by applying to resize, background removal, and cropping functions. In the first experiment, we did not preprocess the dataset and directly apply deep CNN on original images and show the behavior of this method on the unclean dataset. On the other hand, in the second experiment first, preprocess datasets and then apply deep CNN for the classification of these diseases. apple leaf diseases are also classified by using AlexNet and the inception V3 model lastly 19 layer CNN is compared with two other models named Alexnet and Inception V3 model both are transfer learning models Results of the AlexNet model is quietly better than the CNN and Inception V3 model is not perform well as compare to both algorithms.