Classification of Citrus Diseases Using Optimization Deep Learning Approach.

Ahmed Elaraby,Saad Alanazi,Walid Hamdy,Deepika Koundal

doi:10.1155/2022/9153207

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.

Highlights

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. e 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. e proposed work describes an approach for detecting and classifying diseases in citrus plants using deep learning and image processing. e main cause of decreased productivity is considered to be plant diseases, which results in financial losses
Computer vision and image processing techniques have become increasingly popular for the detection and classification of plant diseases. e suggested approach is evaluated on the citrus disease image gallery dataset and the combined dataset. ese datasets were used to identify and classify citrus diseases such as anthracnose, black spot, canker, scab, greening, and melanose
Plant diseases have a wide variety of effects, from minor symptoms to full plantation loss, all of which have a significant influence on the agricultural economy [2]

Summary

Introduction

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. e 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. e proposed work describes an approach for detecting and classifying diseases in citrus plants using deep learning and image processing. e main cause of decreased productivity is considered to be plant diseases, which results in financial losses. E proposed work describes an approach for detecting and classifying diseases in citrus plants using deep learning and image processing. As the most significant element of citrus disease processing, is progressively performed by machine learning than manual techniques such as computer image processing, pattern recognition, and other technologies. Deep learning is being utilized extensively in agriculture for the identification and categorization of plant diseases [8]. E available dataset for training deep learning models has a significant impact on their performance. E datasets currently available for citrus plant diseases usually lack sufficient images in a variety of situations, which are required for developing high-accuracy models. E major goal of this research is to use deep learning approaches to identify citrus plant diseases at a lower cost. We used stochastic gradient descent with momentum to optimize the models

Methods

Results

Conclusion