scholarly journals Review and Further Prospects of Plant Disease Detection Using Machine Learning

2021 ◽  
pp. 105-115
Author(s):  
Vempati Ramsanthosh ◽  
Anati Sai Laxmi ◽  
Chepuri Sai Abhinay ◽  
Vadepally Santosh ◽  
Vybhav Kothareddy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Sustainable Development ◽  
Feature Extraction ◽  
Processing Time ◽  
Plant Disease ◽  
Health Surveillance ◽  
Plant Diseases ◽  
Disease Detection ◽  
Machine Learning Methods

Identifying of the plant diseases is essential in prevention of yield and volume losses in agriculture Product. Studies of plant diseases mean studies of visually observable patterns on the plant. Health surveillance and detecting diseases in plants is essential for sustainable development agriculture. It is very difficult to monitor plant diseases manually. It requires a lot of experiences in work, expertise in these field plant diseases and also requires excessive processing time. Therefore; image processing is used to detect plant diseases. Disease detection includes steps such as acquisition, image Pre-processing, image segmentation, feature extraction and Classification. We describe these methods for the detection of plant diseases on the basis of their leaf images; automatic detection of plant disease is done by the image processing and machine learning. The different leaf images of plant disease are collected and feature extracted of the various machine learning methods.

scholarly journals A Review on Advances in Automated Plant Disease Detection

2021 ◽  
Vol 11 (4) ◽  
pp. 251-264
Author(s):  
Radhika Bhagwat ◽  
Yogesh Dandawate
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Plant Disease ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Plant Diseases ◽  
Detection Methods ◽  
Economic Losses ◽  
Disease Detection ◽  
Range Images

Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images.

Analysis of Plant Disease Detection and Classification Models: A Computer Vision Perspective

2020 ◽  
Vol 17 (12) ◽  
pp. 5422-5428
Author(s):  
K. Jayaprakash ◽  
S. P. Balamurugan
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Plant Disease ◽  
Weather Conditions ◽  
Vital Role ◽  
Plant Diseases ◽  
Disease Detection ◽  
Classification Models ◽  
Extraction Techniques ◽  
Identification Process

Presently, rapid and precise disease identification process plays a vital role to increase agricultural productivity in a sustainable manner. Conventionally, human experts identify the existence of anomaly in plants occurred due to disease, pest, nutrient deficient, weather conditions. Since manual diagnosis process is a tedious and time consuming task, computer vision approaches have begun to automatically detect and classify the plant diseases. The general image processing tasks involved in plant disease detection are preprocessing, segmentation, feature extraction and classification. This paper performs a review of computer vision based plant disease detection and classification techniques. The existing plant disease detection approaches including segmentation and feature extraction techniques have been reviewed. Additionally, a brief survey of machine learning (ML) and deep learning (DL) models to identify plant diseases also takes place. Furthermore, a set of recently developed DL based tomato plant leaf disease detection and classification models are surveyed under diverse aspects. To further understand the reviewed methodologies, a detailed comparative study also takes place to recognize the unique characteristics of the reviewed models.

Deep Learning in Plant Diseases Detection for Agricultural Crops

2020 ◽  
Vol 11 (2) ◽  
pp. 41-58 ◽  
Author(s):  
Mohamed Loey ◽  
Ahmed ElSawy ◽  
Mohamed Afify
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Plant Disease ◽  
Plant Diseases ◽  
Food Plants ◽  
Disease Detection ◽  
Human Society ◽  
Research Papers ◽  
Limited Scope

Deep learning has brought a huge improvement in the area of machine learning in general and most particularly in computer vision. The advancements of deep learning have been applied to various domains leading to tremendous achievements in the areas of machine learning and computer vision. Only recent works have introduced applying deep learning to the field of using computers in agriculture. The need for food production and food plants is of utmost importance for human society to meet the growing demands of an increased population. Automatic plant disease detection using plant images was originally tackled using traditional machine learning and image processing approaches resulting in limited accuracy results and a limited scope. Using deep learning in plant disease detection made it possible to produce higher prediction accuracies as well as broadened the scope of detected diseases and plant species considered. This article presents a survey of research papers that presented the use of deep learning in plant disease detection, and analyzes them in terms of the dataset used, models employed, and overall performance achieved.

scholarly journals Assessment of Plant Disease Identification using GLCM and KNN Algorithms

2020 ◽  
Vol 8 (5) ◽  
pp. 4900-4904
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Plant Disease ◽  
Nearest Neighbors ◽  
Input Image ◽  
Plant Diseases ◽  
Disease Detection ◽  
Textural Features ◽  
K Nearest Neighbors ◽  
Disease Identification

One of the significant segments of Indian Economy is Cultivation. Occupation to almost 50% of the nation’s labor force is delivered by Indian cultivation segment. India is recognized to be the world's biggest manufacturer of pulses, rice, wheat, spices and spice harvests. Agronomist's financial progress is contingent on the excellence of the goods that they yield, which depend on on the plant's progress and the harvest they get. Consequently, in ground of cultivation, recognition of disease in plants shows an involved part. Plants are exceedingly disposed to to infections that disturb the progress of the plant which in chance distresses the natural balance of the agronomist. In order to distinguish a plant disease at right preliminary period, usage of automatic disease detection procedure is beneficial. The indications of plant diseases are noticeable in various portions of a plant such as leaves, etc. Physical recognition of plant disease by means of leaf descriptions is a wearisome job. The k-mean clustering procedure is utilized for the segmentation of input images. The GLCM (gray-level co-occurrence matrices) procedure is utilized which excerpts textural features from the input image and implementation of KNN (k-nearest neighbors) algorithm for image classification and produced classification accuracy from 70 to 75% for different inputs. Hence, it is required to develop machine learning based computational methods which will make the process of disease detection and classification using leaf images automatic. .. To advance concert of standing methods machine learning and deep learning algorithms will be utilized for more accurate classification.

scholarly journals CNN based Leaf Disease Identification and Remedy Recommendation System

2021 ◽  
Vol 9 (8) ◽  
pp. 953-957
Author(s):  
Mr. Yeresime Suresh
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Crop Yield ◽  
Plant Disease ◽  
Recommendation System ◽  
Plant Diseases ◽  
Human Intervention ◽  
Traditional Methods ◽  
Disease Identification ◽  
Growing Population

Abstract: In this present era, agriculture has become simply a means to feed ever growing population. It is very important where in more than 70% population depends on agriculture in India. The plant diseases effect the humans directly or indirectly by health and also economically. To detect these plant diseases we need an automatic way without much of human intervention. We attempt to analyse the disease using image processing and automate the detection by implementing machine learning methodology. Traditional methods were used to detect the diseases which lead to the use of large amount of pesticides harming the fertile soil and also the nature. A solution to this is to use current methodologies like Image Processing and Machine Learning that helps the farmers to detect the diseases faster and increase the crop yield. Keywords: Plant Disease, Remedy, Image Processing

scholarly journals Plant Disease Detection Using Leaf Images

2021 ◽  
Vol 9 (VIII) ◽  
pp. 600-602
Author(s):  
Shishira .
Keyword(s):  
Image Processing ◽  
Health Monitoring ◽  
Plant Disease ◽  
Plant Diseases ◽  
Agricultural Product ◽  
Disease Detection ◽  
Data Sets ◽  
Tremendous Amount ◽  
Image Format ◽  
Excessive Quantity

Identification of the plant diseases is that the key to prevent the losses within the yield and quantity of the agricultural product. The studies of the plant diseases mean the studies of visually observable patterns seen on the plant. Health monitoring and disease detection on plant is incredibly critical for sustainable agriculture. It’s very difficult to watch the plant diseases manually. It requires tremendous amount of labor, expertise within the plant diseases, and also require the excessive quantity. Hence, image processing is used for the detection of plant diseases by capturing the pictures of the leaves and comparing it with the data sets. The data sets comprise of different plant within the image format. Except detection users are directed to an e-commerce website where different pesticides with its rate and usage directions are displayed. This website is efficiently used for comparing the MRP’s of varied pesticides and buy the desired one for the detected disease. This paper aims to support and help the green house farmers in an efficient way.

scholarly journals A Smartphone-based Plant Disease Detection and Treatment Recommendation System using Machine Learning Techniques

2022 ◽  
Vol 10 (1) ◽  
pp. 1-8
Author(s):  
Folasade Isinkaye
Keyword(s):  
Machine Learning ◽  
Crop Production ◽  
Plant Disease ◽  
Recommendation System ◽  
Research Effort ◽  
Plant Diseases ◽  
Machine Learning Techniques ◽  
Treatment Recommendation ◽  
Disease Detection ◽  
Recommended Treatment

Plant diseases cause major crop production losses worldwide, and a lot of significant research effort has been directed toward making plant disease identification and treatment procedures more effective. It would be of great benefit to farmers to be able to utilize the current technology in order to leverage the challenges facing agricultural production and hence improve crop production and operation profitability. In this work, we designed and implemented a user-friendly smartphone-based plant disease detection and treatment recommendation system using machine learning (ML) techniques. CNN was used for feature extraction while the ANN and KNN were used to classify the plant diseases; a content-based filtering recommendation algorithm was used to suggest relevant treatments for the detected plant diseases after classification. The result of the implementation shows that the system correctly detected and recommended treatment for plant diseases

scholarly journals Identification of Leaf Disease Using Image Processing Technique

2021 ◽  
pp. 881-885
Author(s):  
Ankit Wagh ◽  
Ritwik Chumble ◽  
Santosh Kangane ◽  
Pranav Bakare
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Plant Disease ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Disease Detection ◽  
Revolutionary Change ◽  
Leaf Disease ◽  
Important Time

The increased availability of smartphones has made it easier for taking technology to every individual. Technology can play important role in the field of agriculture to improve outcomes. Plant disease is one of the important reasons for the decrement of yield. Bridging technology with agriculture can give revolutionary change. It is challenging to monitor the disease of plants manually. It consumes important time, resources, and need efforts. Hence faster image processing technique is used as a solution for it. Disease detection using image processing involves multiple steps like the acquisition of images, pre-processing, segmentation, feature extraction, and classification. This paper discusses a faster image processing technique using the KNN method. This algorithm gives an asymptotically faster method for image processing.

scholarly journals Analysis and Implementation of Fruit/Leave Disease Detection using Image Processing and Neural Approach

2021 ◽  
Vol 7 (09) ◽  
pp. 186-194
Author(s):  
Krishna Madheshiya, Prashant Richhariya and Dr. Anita Soni
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Plant Diseases ◽  
Disease Detection ◽  
New Approach ◽  
Detection Model ◽  
Learning Framework ◽  
Deep Cnn ◽  
First Time ◽  
Leaf Image

The latest generation of convolution neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of fruit/plant disease detection model, based on leaf image processing and classification, by the use of ANN. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training

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