Classification of Citrus Diseases Using Optimization Deep Learning Approach

Abstract

Most plant diseases have apparent signs, and today’s recognized method is for an expert plant pathologist to identify the disease by looking at infected plant leaves using a microscope. The fact is that manually diagnosing diseases is time consuming and that the effectiveness of the diagnosis is related to the pathologist’s talents, making this a great application area for computer-aided diagnostic systems. The proposed work describes an approach for detecting and classifying diseases in citrus plants using deep learning and image processing. The main cause of decreased productivity is considered to be plant diseases, which results in financial losses. Citrus is an important source of nutrients such as vitamin C all around the world. On the contrary, citrus diseases have a negative impact on the citrus fruit and quality. In the recent decade, computer vision and image processing techniques have become increasingly popular for the detection and classification of plant diseases. The suggested approach is evaluated on the citrus disease image gallery dataset and the combined dataset (citrus image datasets of infested scale and plant village). These datasets were used to identify and classify citrus diseases such as anthracnose, black spot, canker, scab, greening, and melanose. AlexNet and VGG19 are two kinds of convolutional neural networks that were used to build and test the proposed approach. The system’s total performance reached 94% at its best. The proposed approach outperforms the existing methods.

An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA–IncRNA Based on Artificial Gorilla Troops Algorithm

“…However, due to their numerous disadvantages, researchers have begun to focus on deep learning methods [ 34 ]. Deep learning has recently become popularly employed for sequence classification [ 35 ], biological information [ 36 ], image processing [ 37 ], computer vision [ 38 ], natural language processing [ 39 ], and other sectors, with positive outcomes.…”

DS-MENet for the classification of citrus disease

“…Zhang et al (2021) proposed an improved convolutional neural network combined with a state transfer algorithm (STA) to identify the surface defects of citrus, and the recognition accuracy of the trained model on the dataset can reach relatively high accuracy. Elaraby et al (2022) used transfer learning to classify citrus diseases based on AlexNet and VGG19, and the proposed method used a momentum stochastic gradient descent algorithm (SGDM) for convergence speed. Janarthan et al (2020) proposed a patch-based framework for citrus disease classification, consisting of an embedding module, a clustering prototype module, and a simple neural network classifier, which achieved promising results in terms of accuracy, parameter size, and time efficiency.…”

Multiclass classifier based on deep learning for detection of citrus disease using fluorescence imaging spectroscopy

“…Recently, several classifiers have been applied to detect plant diseases [25][26][27][28], presenting different accuracies. Multiclass deep learning techniques have also been applied to discriminate citrus plant diseases (anthracnose, black spot, canker, scab, HLB, and melanose) with 94% accuracy [29]. Alanazi et al used two types of convolutional neural networks (CNNs), AlexNet and VGG 19, to identify and classify these citrus diseases, whose images were collected from fruits and leaves [29].…”

“…Multiclass deep learning techniques have also been applied to discriminate citrus plant diseases (anthracnose, black spot, canker, scab, HLB, and melanose) with 94% accuracy [29]. Alanazi et al used two types of convolutional neural networks (CNNs), AlexNet and VGG 19, to identify and classify these citrus diseases, whose images were collected from fruits and leaves [29]. Although such work is interesting, it presents some disadvantages: (i) the samples are a mixture of fruits and leaves.…”