scholarly journals Plant Disease Prediction System using Image Processing

2020 ◽  
pp. 162-167
Author(s):  
B Uma Jagadeswari ◽  
D Harshitha ◽  
G Vineela ◽  
B Siri ◽  
Yaragani Sowmya
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Negative Impact ◽  
Agricultural Productivity ◽  
Significant Loss ◽  
Plant Diseases ◽  
Disease Prediction ◽  
Agricultural Field ◽  
Processing Techniques ◽  
Leaf Image

Agriculture productivity is the major issue which affects the Indian economy. Crop cultivation plays an essential role in the agricultural field. Presently, the loss of food is mainly due to infected crops, which reflexively reduces the production rate. The major cause for decrease in the quality and amount of agricultural productivity is due to the diseases in plants. The occurrence of diseases in plants may result in significant loss in both quality and quantity of agricultural productivity. This can produce the negative impact on the countries whose economies are primarily dependent on the agriculture. Farmers encounter great difficulties in detecting and controlling plant diseases. Hence the detection of plant diseases in the earlier stages is very important to avoid the loss in terms of quality, quantity and finance. This paper mainly focuses on the approach based on image processing techniques that help farmers for detecting the diseases of plants by uploading leaf image to the system.

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

scholarly journals Plant Leaf Disease Prediction

2021 ◽  
Vol 9 (VII) ◽  
pp. 1295-1305
Author(s):  
Vaishnavi Monigari
Keyword(s):  
Plant Disease ◽  
Accurate Method ◽  
Significant Loss ◽  
Plant Diseases ◽  
Economic Losses ◽  
Healthy Plant ◽  
Plant Leaves ◽  
Production Economic ◽  
Agricultural Yield ◽  
Processing Techniques

The Indian economy relies heavily on agriculture productivity. A lot is at stake when a plant is struck with a disease that causes a significant loss in production, economic losses, and a reduction in the quality and quantity of agricultural products. It is crucial to identify plant diseases in order to prevent the loss of agricultural yield and quantity. Currently, more and more attention has been paid to plant diseases detection in monitoring the large acres of crops. Monitoring the health of the plants and detecting diseases is crucial for sustainable agriculture. Plant diseases are challenging to monitor manually as it requires a great deal of work, expertise on plant diseases, and excessive processing time. Hence, this can be achieved by utilizing image processing techniques for plant disease detection. These techniques include image acquisition, image filtering, segmentation, feature extraction, and classification. Convolutional Neural Network’s(CNN) are the state of the art in image recognition and have the ability to give prompt and definitive diagnoses. We trained a deep convolutional neural network using 20639 images on 15 folders of diseased and healthy plant leaves. This project aims to develop an optimal and more accurate method for detecting diseases of plants by analysing leaf images.

SOYBEAN LEAF DISEASES DETECTION AND CLASSIFICATION USING RECENT IMAGE PROCESSING TECHNIQUES

2020 ◽  
Vol 8 (3) ◽  
pp. 01-08
Author(s):  
Arpan Singh Rajput ◽  
Shailja Shukla ◽  
S. S. Thakur
Keyword(s):  
Image Processing ◽  
Early Stage ◽  
Weather Condition ◽  
Plant Diseases ◽  
Soybean Plant ◽  
Plant Leaf ◽  
Image Processing Techniques ◽  
Soybean Leaf ◽  
Processing Techniques ◽  
Soybean Diseases

Purpose: India is an agricultural country and soybean production is one of the major sources of earning. Due to the major factors like diseases, pest attacks, and sudden changes in the weather condition, the productivity of the soybean crop decreases. Automatic detection of soybean plant diseases is essential to detect the symptoms of soybean diseases as early as they appear on the growing stage. This paper proposed a methodology for the analysis and detection of soybean plant leaf diseases using recent digital image processing techniques. In this paper, experimental results demonstrate that the proposed method can successfully detect and classify the major soybean diseases. Methodology: MatLab 18a is used for the simulation for the result and machine learning-based recent image processing techniques for the detection of the soybean leaf disease. Main Findings: The main finding of this work is to create the soybean leaf database which includes healthy and unhealthy leaves and achieved 96 percent accuracy in this work using the proposed methodology. Applications of this study: To detect soybean plant leaf diseases in the early stage in Agricultural. The novelty of this study: Self-prepared database of healthy and unhealthy images of soybean leaf with the proposed algorithm.

scholarly journals Lemon Leaf Disease Detection and Classification using SVM and CNN

2019 ◽  
Vol 8 (4) ◽  
pp. 11485-11488
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Color Space ◽  
Input Image ◽  
Plant Diseases ◽  
Image Processing Technique ◽  
Support Vector ◽  
Training Time ◽  
Leaf Disease

India is a developing country and agriculture has always played a major role in bolstering the country’s economic growth. Due to various factors like industrialization, mechanization and globalization, the green fields are facing complications. So, identifying the plant disease incorrectly will lead to a huge loss of both quantity and quality of the product and it will also incur loss in time and money. Hence, identifying the condition of the plant plays a major role for successful cultivation. Now a day’s image processing technique is being employed as a focal technique for diagnosing the various features of the crop. The image processing techniques can be used for identification of the plant disease and hence classify the plant disease. Generally, the symptoms of the disease are observed on leaves, stems, flowers etc. Here, the leaves of the affected plant are used for the identification and classification of the disease. Leaf image is captured using a smart phone as the first step and then they are processed to determine the condition of the plant. Identification of plant disease follows the steps like loading the image of the plant leaf, histogram equalization for enhancing contrast of the image, segmentation process by using Lab color space model, extracting features of the segmented image using GLCM (Grey Level Cooccurrence Matrix) and finally classification of leaf disease by using MCSVM (Multi Class Support Vector Machine).This procedure obtained an accuracy percentage of 83.6%.Also, it takes long training time for large datasets. To improve the accuracy of the detection and the classification of the plants, Convolutional Neural Network (CNN) is used. The main advantage of CNN is that it automatically detects the main features of the input without any supervision of human. In CNN identification of disease follow the steps like loading the image as the input image, convolution of the feature map and finally max pooling the layers to calculate the features of the image in detail. The plant diseases are classified with an accuracy of 93.8 %.

scholarly journals Detection of Cotton Leaf Disease Using Image Processing Techniques

2021 ◽  
Vol 2062 (1) ◽  
pp. 012009
Author(s):  
Sushreeta Tripathy
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Region Of Interest ◽  
Texture Features ◽  
Disease Diagnosis ◽  
Cotton Leaf ◽  
Diagnosis System ◽  
Image Processing Techniques ◽  
Plant Disease Diagnosis ◽  
Processing Techniques

Abstract In the area of research, diagnosis of disease symptoms in the plants duly applying image processing methods is a matter of big concern. The need of the hour is to prepare an efficient plant disease diagnosis system that can help the farmers in their cultivation and farming. This work is an attempt to prepare a framework of plant disease diagnosis system by using the cotton plant leaves. The digital pictures of cotton leaves are obtained to undergo a set of image processing techniques. Thresholding based segmentation techniques are used to remove the region of interest (ROI) i.e., infected part from the enhanced images. Consequently, diseases are detected from the region of interest by using an accurate set of visual texture features. At last treatment actions are taken to supervise the diseases found in the plants. This work will help the farmer’s society to take effective measures to protect their crops from diseases.

scholarly journals A Review On Leaves Disease Segmentation And Classification Techniques

2019 ◽  
pp. 132-140
Author(s):  
Soumya Ranjan Sahu ◽  
Chandra Sekhar Panda
Keyword(s):  
Image Processing ◽  
Human Health ◽  
Traditional Method ◽  
Plant Diseases ◽  
Image Processing Techniques ◽  
Naked Eye ◽  
The People ◽  
Different Types ◽  
Processing Techniques

Agriculture plays a major role in our society. Most of the people depend on agriculture for their living. It becomes very important part of society for their livelihood. But there are some problems on agriculture that directly or indirectly affect the human health and also economy. The major problem for agriculture is the plant diseases. This paper is based on a survey of different types of techniques used for segmenting and classification of plant diseases by using image processing techniques. By these techniques, we can easily detect the area of the infected part or can identify the type of disease. This paper gives various techniques used by various authors to detect the disease fast an accurately. They used different types of segmentation techniques like region based, clustering, thresolding etc. to detect the infected part of the leaves and by using the classifier they classify the disease name. The traditional method of naked eye observation can be overcome by introducing these methods. Main focus of our work is to analysis of fast and accurate techniques to identify the plant diseases.

scholarly journals Agriculture Crop Leaf Disease Detection using Image Processing

2021 ◽  
Vol 10 (7) ◽  
pp. 110-114
Author(s):  
Venkatesh T. ◽  
Prathyush K. ◽  
Deepak* S. ◽  
U.V.S.A.M. Preetham
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Plant Diseases ◽  
Huge Amount ◽  
Or Efficiency ◽  
Plant Leaf ◽  
Image Processing Techniques ◽  
The One ◽  
Processing Techniques

As we all know that the Agriculture plays an important role in the Indian economy and majority of the individuals depends upon it and offers huge amount of the crops through the worldwide. The Illnesses in these crops are generally on the leaf's influences on the decrease of both quality and number of horticultural items. We should know the disease of the crop correctly to solve the problem. There will be a huge loss if we do not find the disease and treat properly. The view of natural eye isn't so a lot more grounded in order to watch minutevariety in the contaminated piece of leaf. In thisreport, we are giving a programming answer fornaturally identify and arrange plant leaf diseases. In this we are utilizing picture preparing methods to characterize alignments and rapidly finding can be completed according to infection. This methodology will upgrade the efficiency of yields in a efficient way and can get us the accurate disease which helps us to find the solution for the diseased crop. It observes a few stages with the help of these pictures obtaining, picture pre-handling, division, highlights extraction and genetic algorithm-based grouping. Relating to the cultivation of land, efficiency is something on which economy exceptionally depends. This is the one of the reasons that sickness identification in plants assumes a significant job in the agriculture business field, as having the illness in plants are very normal. In an event that legitimate consideration isn't taken here, at that point it causes true consequences for plantsand because of which quality of each and every item, amount or efficiency is being influenced. The recognition of plant infections through some programmed step is gainful as it avoids a huge work of checking in huge homesteads of harvests. At the beginning of the crop harvesting step itself, it shows the side effects or the symptoms of the diseases. This proposed method surfaces into a new programmed manner by distinguishing the effects of the crop plant diseases. We are using some image processing techniques for the identification of the disease. Additionally, it watches the review on the various diseases order strategies which also can be utilized for plant leaf alignment. Picture division, which is a significant viewpoint for sickness identificationin a plant leaf alignment, is finalized by the input RGB mask images.

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