A Novel Convolutional Neural Network Based Model for Recognition and Classification of Apple Leaf Diseases

Abstract

Plants have a great role to play in biodiversity sustenance. These natural products not only push their demand for agricultural productivity, but also for the manufacturing of medical products, cosmetics and many more. Apple is one of the fruits that is known for its excellent nutritional properties and is therefore recommended for daily intake. However, due to various diseases in apple plants, farmers have to suffer from a huge loss. This not only causes severe effects on fruit’s health, but also decreases its overall productivity, quantity, and quality. A novel convolutional neural network (CNN) based model for recognition and classification of apple leaf diseases is proposed in this paper. The proposed model applies contrast stretching based pre-processing technique and fuzzy c-means (FCM) clustering algorithm for the identification of plant diseases. These techniques help to improve the accuracy of CNN model even with lesser size of dataset. 400 image samples (200 healthy, 200 diseased) of apple leaves have been used to train and validate the performance of the proposed model. The proposed model achieved an accuracy of 98%. To achieve this accuracy, it uses lesser data-set size as compared to other existing models, without compromising with the performance, which become possible due to use of contrast stretching pre-processing combined with FCM clustering algorithm.

Apple Disease Recognition Based on Convolutional Neural Networks With Modified Softmax

“…To validate the effectiveness of the proposed method, a number of plant disease recognition experiments are carried on two diseased leaf image databases of apple, namely, YangLing and Kaggle, and compared with Plant leaf disease recognition using SVM and PHOG Descriptor (SVMPHOG; Zhang and Sha, 2013 ), Plant leaf disease recognition using K-means-based segmentation and neural networks (KSNN; Bashish et al, 2011 ), Plant leaf disease recognition using image recognition technology (IRT; Qin et al, 2016 ), AlexNet ( Jg et al, 2021 ), LeNet5 ( Qi et al, 2021 ), VGG-16 ( Dhaka et al, 2021 ), and CNN ( Yadav et al, 2020 ). SVMPHOG, KSNN, and IRT are three feature extraction-based methods, where each leaf image is firstly segmented and their recognition rates rely heavily on the image segmented and feature extraction.…”

“…AlexNet, LeNet5, VGG-16, and CNN are four kinds of widely used models. The parameters of the pre-training network in the constructed MCNN are set as: the number of training iterations 3,000, the number of samples processed per time Bsize = 128, weight decay is 0.0005, momentum is 0.9, and the initial learning rate is 0.001, which is reduced to half of the original value every 1,000 steps ( Yadav et al, 2020 ), and Adam is as optimizer ( Kingma and Ba, 2017 ). To verify the effectiveness of the proposed algorithm, the image recognition accuracy and average processing time per image are used to evaluate the algorithm.…”

“…Based on the experimental results, they observed that the “Custom-Net” extracts the relevant features and the transfer learning improves the extraction of relevant features. Yadav et al (2020) proposed a CNN-based recognition method of apple leaf diseases. They applied the contrast stretching based preprocessing approach and fuzzy C-means (FCM) clustering algorithm to segment the disease leaf images, utilized CNN to recognized apple leaf diseases, and validated the performance of the proposed model using 400 image samples (200 healthy, 200) of leaves.…”

A Novel Deep Convolutional Neural Network Based on ResNet‐18 and Transfer Learning for Detection of Wood Knot Defects

“…Consequently, it is important to quickly identify the wood knot defects in a short time [1][2][3][4]. Although the traditional manual recognition is widely used and accurate, it is still a subjective [5] and inefficient method to identify wood knot defects [6]. With the rapid development of digital image processing and computer vision, artificial intelligence technology can improve the recognition speed and accuracy at a certain extent [7][8][9].…”

BLNN: Multiscale Feature Fusion‐Based Bilinear Fine‐Grained Convolutional Neural Network for Image Classification of Wood Knot Defects

“…Consequently, it is important to identify the defects of wood knots in a short time. Although manual recognition is accurate, it takes a lot of time to train the staff, and the recognition speed on the assembly line is very slow compared to machine recognition [4,5]. With the development of artificial intelligence and computer vision technology, deep learning has potential significance in the application of wood knot defect classification [6][7][8].…”