Automatic Detection and Recognition of Citrus Fruit & Leaves Diseases for Precision Agriculture

Abstract

Machine learning is a branch of computer science concerned with developing algorithms & models capable of ‘learning through data and iterations’. Deep learning simulates the structure and function of human organs and diseases using artificial neural networks with more than one hidden layer. The primary purpose of this work is to develop and test computer vision and machine learning algorithms for classifying Huanglongbing (HLB)-infected, healthy, and unhealthy leaves and fruits of the citrus plant. The images were segmented using a normalized graph cut, and texture information was extracted using a co-occurrence matrix. The collected attributes were used for classification and support vector machine (SVM), and deep learning methods were employed. When rating the classification outcomes, the accuracy of the classification and the number of false positives and false negatives were considered. The result shows that Deep Learning could create categories up to 96.8% of HLB-infected leaves and fruits. Despite a broad variance in intensity from leaves collected in North India, this method suggests it could be beneficial in diagnosing HLB.

E-Citrus: A Cloud-Based Citrus Pest and Disease Detection, Diagnostic and Prevention using Convolutional Neural Network

“…Additionally, a survey by Saini et al [1] regarding various approaches for detecting and classifying diseases in citrus plant leaves revealed that image preprocessing strategies contribute to increasing segmentation precision. During this survey, the k-means technique was the most critical segmentation strategy for diseased plants.…”

“…Any nation's economic development depends heavily on agricultural productivity. Saini et al [1] stated that disease identification in plants is vital in the agricultural sector, as plant disease is common. Thus, the development of tools to early detect pests and diseases would be a great tool to prevent their continuous widespread.…”

Rumex Weed Classification Using Region-Convolution Neural Networks Based-Colour Space Information

“…Recently, weed and crop detection have attracted many researchers to propose and develop methods using different machine learning and Deep Learning (DL) techniques, for example, automated classification of crop and weed [23,20,30,18,3], segmentation of weed-crop [31]. In [22], Convolutional Neural Networks (CNNs) was proposed to classify the plants into dock, clover and grass and to solve formidable challenges due to the images complexity.…”

"A Comparative Analysis of Deep Learning Algorithms for Fruit Disease Classification"

“…Every sector of the economy relies on this function. sincerely want to increase fruit output on farmland, then must make sure that fruit farming is sustainable in terms of disease control [3,4]. The amount of minerals and nutrients inside fruits has a considerable impact on the cultivation methods employed for apples, oranges, grapefruits, pomegranates, and plums.…”