Detection of citrus leaf diseases using a deep learning technique

Abstract

The food security major threats are the diseases affected in plants such as citrus so that the identification in an earlier time is very important. Convenient malady recognition can assist the client with responding immediately and sketch for some guarded activities. This recognition can be completed without a human by utilizing plant leaf pictures. There are many methods employed for the classification and detection in machine learning (ML) models, but the combination of increasing advances in computer vision appears the deep learning (DL) area research to achieve a great potential in terms of increasing accuracy. In this paper, two ways of conventional neural networks are used named Alex Net and Res Net models with and without data augmentation involves the process of creating new data points by manipulating the original data. This process increases the number of training images in DL without the need to add new photos, it will appropriate in the case of small datasets. A self-dataset of 200 images of diseases and healthy citrus leaves are collected. The trained models with data augmentation give the best results with 95.83% and 97.92% for Res Net and Alex Net respectively.

Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model

“…Due to various parameter configurations of layers in a convolutional neural network like filters, filter size, batch size, number of epochs, and various measures like accuracy, recall, and F1 measure, the proposed approach outperformed the baseline work. Luaibi et al [7] claim an accuracy of 93.75 percent on the given dataset, but we found an accuracy of 92.20 percent on the same dataset when we experimented.…”

“…The results also show that the maximum accuracy was achieved through the use of CNN with various convolution layers. The aforesaid results demonstrate that, in terms of Citrus disease recognition, the suggested CNN model reached the best performance level (94.55% accuracy) than the other deep and machine-learning work [7,23,24,32]. The proposed model's best accuracy over the benchmark works is due to the use of CNN, which is intended for image processing.…”

“…The efficacy of the Deep Learning-based CNN model is compared to the work introduced by Luaibi et al [7]. Due to various parameter configurations of layers in a convolutional neural network like filters, filter size, batch size, number of epochs, and various measures like accuracy, recall, and F1 measure, the proposed approach outperformed the baseline work.…”

“…[7].The main disadvantage of the machine learning-based SVM model is that it performs poorly and achieves lower accuracy than the deep learning model, despite the fact that CNN produces impressive results for image classification. On the benchamark dataset, Patil et al [7] achieved an accuracy of 89.46 percent. When tested on the same dataset, however, we got an accuracy of 55.58 percent.…”

“…The development of sophisticated tools and rapid computer-assisted techniques have made it easier to scan and automatically detect anomalies in a crop in real time [4]. Conventional machine learning techniques have had considerable success in recognizing and diagnosing plant disease, but they are limited to the following sequential image processing tasks: image segmentation using clustering and other methods [5,6], feature extraction [7], and pattern recognition using support vector machines (SVM) [8], k-nearest neighbor method [9], and Artificial Neural Network [10]. It is difficult to pick and extract the best observable pathological characteristics, necessitating the use of highly qualified engineers and experienced specialists, which is not only arbitrary but also inefficient of manpower and financial capital.…”