scholarly journals Detection and Classification of Paddy Crop Disease using Deep Learning Techniques

2019 ◽  
Vol 8 (3) ◽  
pp. 4353-4359
Keyword(s):  
Crop Production ◽  
Input Image ◽  
Vital Role ◽  
Disease Detection ◽  
Efficient Detection ◽  
Learning Techniques ◽  
Crop Disease ◽  
Time Basis ◽  
Occurrence Matrix ◽  
Paddy Crop

Agricultural production plays a vital role in Indian economy. The biggest menace for a farmer is the various diseases that infect the crop. Quality and high production of crops is involved with factors like efficient detection of diseases in the crop. The disease detection though Naked-eye observation of expert can be prohibitively expensive and requires meticulous and scrupulous analysis to detect the disease. The existing systems on disease detection is not efficient enough in terms on real time basis. This paper presents an effective method for identification of paddy leaf disease. The proposed approaches involves pre-processing of input image and the paddy plant disease type is recognized using Gray-Level Co-occurrence Matrix (GLCM) technique and classifiers namely Artificial Neural Networks is used for better accuracy of detection. This method will be very useful to farmers to detect paddy diseases beforehand and thus prevent over usage of pesticides which in turn affects the crop production

scholarly journals An Agri vigilance System based on Computer Vision and Deep Learning

2021 ◽  
Vol 2 (2) ◽  
pp. 1-8
Author(s):  
Karan Owalekar ◽  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
National Income ◽  
Efficient Solutions ◽  
Vital Role ◽  
Disease Detection ◽  
Detailed Report ◽  
Crop Monitoring ◽  
Crop Disease ◽  
Area Of Concern

In an agricultural-based country like India, farming and farming activities play a vital role in the growth of the economy as it is the main source of GNI (Gross National Income). This dependence of GNI on agriculture makes it important to address the issues faced by the farmers. The main area of concern for farmers revolves around crops and livestock. Precise farming techniques like cattle counting and crop disease detection are the need of the hour. The introduction of computer vision and deep learning has enabled us to make improvements in farming techniques. To accomplish this, a computer vision-based system is proposed which will be implemented using ResNet and YOLOv3-tiny. The proposed system will take images and videos as input and run them on the inference. The output will be updated in the database and the farmer will be notified in case of any inconsistency. The detailed report can be accessed by government agencies. The system will increase efficiency in farming processes like crop monitoring, livestock tracking, crop disease detection by providing fast and efficient solutions for the problems faced by the farmers.

Survey Paper on Tomato Crop Disease Detection and Pest Management

2019 ◽  
Vol 10 (3) ◽  
pp. 10-18
Author(s):  
Abhijeet Somnath Gurle ◽  
Sankalp Nitin Barathe ◽  
Roshan Shankar Gangule ◽  
Shubham Dipak Jagtap ◽  
Tanuja Patankar
Keyword(s):  
Pest Management ◽  
Crop Production ◽  
Disease Detection ◽  
Important Research ◽  
Tomato Crop ◽  
Survey Paper ◽  
Major Threat ◽  
Important Research Topic ◽  
Crop Disease ◽  
Tomato Diseases

India is an agricultural country and most of peoples wherein about 70% depends on agriculture. So, disease detection is very important research topic. There are many species of tomato diseases and pests, the pathology of which is complex. Crop diseases are a major threat to crop production, but their identification remains difficult in many parts of India due to the lack of the necessary infrastructure. It is difficult and error-prone to simply rely on manual identification. Recent advances in computer vision made possible by deep learning has made the way for automatic disease detection. In this article, the authors have analysed a method of disease detection and pest management using a convolution neural networks (CNN), k-means clustering, and acoustic emission.

Pattern Recognition Using Image Processing

2018 ◽  
pp. 277-282 ◽  
Author(s):  
Gayathri J ◽  
Ramya S
Keyword(s):  
Image Processing ◽  
Rural Areas ◽  
Agricultural Sector ◽  
Processing System ◽  
Disease Detection ◽  
Bacterial Diseases ◽  
Crop Disease ◽  
Paddy Cultivation ◽  
Paddy Crop ◽  
Detection And Recognition

Paddy cultivation plays an important role in agriculture. But the growth of crop is affected by various diseases. If detection of disease is not properly done at earlier stage, then it may result in decrease of paddy production. India is agriculture based country and it provides employment to peoples in rural areas.. The agricultural sector plays major role in development of our economy by providing employment for rural peoples. Paddy is the staple food of Indians and hence it is considered as nation’s important product. Crop management is followed to protect paddy plants from fungal and bacterial diseases. The main goal is to develop an image processing system to identify and classify the various diseases affecting the growth of paddy plants. The work is divided into two parts paddy crop disease detection and recognition of paddy crop diseases. Disease detection technique is used to detect the disease affected portion in the paddy plant. The techniques used to detect diseased portions of paddy crop are Boundary localization and Haar-like features methods and neural network is employed based on diseases classification.

Application of Machine Learning Techniques in Crop Disease Classification: A Comprehensive Review

2021 ◽  
Vol 9 (1) ◽  
pp. 89-93
Author(s):  
Khwairakpam Amitab ◽  
◽  
Lal Hmingliana ◽  
Amitabha Nath ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Systems ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Disease Detection ◽  
Comprehensive Review ◽  
Road Map ◽  
Learning Techniques ◽  
Crop Disease

Crop diseases are the main threat to agricultural products. Fast, accurate, and automatic detection of diseases can help to overcome this problem. Literature suggests, machine learning techniques are capable of achieving these goals in near real-time. This article presents a comprehensive review of machine learning techniques for crop disease detection and classification. Existing plant disease classification systems are explored and commonly used processing steps are investigated. Analysis of machine learning techniques, accuracy factor, and current state-of-the-art in this domain have been reviewed and presented through this manuscript. The survey resulted in the identification of the strengths and limitations of existing techniques and provides a road map for future research works. These would help researchers to understand and pursue machine learning applications in the field of disease detection and classification

scholarly journals Analysis of Crop disease detection with SVM, KNN and Random forest classification

2021 ◽  
Vol 9 (1) ◽  
pp. 364-372
Author(s):  
MRS. RUPALI KALE, MR. SANJAY SHITOLE
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Clustering Algorithm ◽  
Image Acquisition ◽  
Region Of Interest ◽  
Input Image ◽  
Vital Role ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Crop Disease

Due to an uneven climatic condition crops are being affected which leads to decrease in agriculture yield. It greatly affects global agricultural economy. However, the condition becomes more worse when diseases are identified in crops. Agriculture plays a vital role in every country’s economy. Thus, there is a need to identify the crop disease before it is visible on a crop so that disease can be avoided by using appropriate measures. The traditional way of identifying a crop disease is through observation by naked eyes. But as it requires large number of experts and continuous monitoring of crop it will be costly for large fields. Hence, an automatic system is required which can not only examine the crops to detect disease but also can classify the type of disease on crops. The proposed system determines disease from an input image. The input image has to go through following stages: Image Acquisition, Image pre-processing, Image segmentation, Feature Extraction, and Classification in order to determine diseased crop and accordingly provides remedy for that disease. Infected crop image is taken as input in Image Acquisition stage. In Image pre-processing stage noise is removed from the input image by applying gaussian blur filter and non-local means denoising algorithm. Also, the background of image is eliminated using Thresholding algorithm. To extract Region of Interest (ROI) i.e. infected region from input image K-means Clustering algorithm is used. Then color, texture and shape features are extracted from ROI and features are further send to the classification stage. Three different classification algorithms namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest are implemented for classification out of which Support Vector Machine Algorithm is found to be best in terms of accuracy. Hence, classification is carried out by using Multivariate Support Vector Machine algorithm which detect disease present in crop accurately. In this way, the proposed system detects a disease from the given input image.

scholarly journals A Framework for Crop Disease Detection Using Feature Fusion Method

2021 ◽  
Vol 11 (3) ◽  
pp. 216-228
Author(s):  
Radhika Bhagwat ◽  
Yogesh Dandawate
Keyword(s):  
Feature Fusion ◽  
Detection Methods ◽  
Disease Detection ◽  
Learning Techniques ◽  
Proposed Model ◽  
Image Patches ◽  
Crop Disease ◽  
Rgb Images ◽  
Cepstral Coefficients ◽  
High Level

Crop disease detection methods vary from traditional machine learning, which uses Hand-Crafted Features (HCF) to the current deep learning techniques that utilize deep features. In this study, a hybrid framework is designed for crop disease detection using feature fusion. Convolutional Neural Network (CNN) is used for high level features that are fused with HCF. Cepstral coefficients of RGB images are presented as one of the features along with the other popular HCF. The proposed hybrid model is tested on the whole leaf images and also on the image patches which have individual lesions. The experimental results give an enhanced performance with a classification accuracy of 99.93% for the whole leaf images and 99.74% for the images with individual lesions. The proposed model also shows a significant improvement in comparison to the state-of-art techniques. The improved results show the prominence of feature fusion and establish cepstral coefficients as a pertinent feature for crop disease detection.

scholarly journals Detection of soilborne disease utilizing sensor technologies: Lessons learned from studies on stem rot of peanut

2021 ◽  
Author(s):  
Xing Wei ◽  
Marcela Aguilera ◽  
Rachael Walcheck ◽  
Dorothea Tholl ◽  
Song Li ◽  
...  
Keyword(s):  
Crop Production ◽  
Plant Disease ◽  
Lessons Learned ◽  
Plant Diseases ◽  
Stem Rot ◽  
Disease Detection ◽  
Soilborne Disease ◽  
Current State ◽  
Major Constraint ◽  
Crop Disease

Soilborne plant diseases are a major constraint to crop production worldwide. Effective and economical management of these diseases is dependent on the ability to accurately detect and diagnose their signs and/or symptoms prior to widespread development in a crop. Sensor-based technologies are promising tools for automated crop disease detection, but research is still needed to optimize and validate methods for the detection of specific plant diseases. The overarching goal of our research is to use the peanut-stem rot plant disease system to identify and evaluate sensor-based technologies that can be utilized for the detection of soilborne plant diseases. Here we summarize the current state of sensor-based technologies for plant disease detection and provide examples from our own research that illustrate the advantages and limitations of different sensor-based methods for detecting soilborne diseases. In addition, the potential to adapt different sensor-based technologies to practical use in the field is discussed.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

Crop Disease Detection in Image Processing Using MATLAB

2020 ◽  
Vol 24 (04) ◽  
pp. 2967-2973
Author(s):  
Archana P ◽  
Hari prabhu S ◽  
Mohammed safir A ◽  
Naveenraj K ◽  
Pravin kumar S
Keyword(s):  
Image Processing ◽  
Disease Detection ◽  
Crop Disease
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