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.

Application of Computer Vision Technology in Agricultural Field

2013 ◽  
Vol 462-463 ◽  
pp. 72-76 ◽  
Author(s):  
Fang Zhang ◽  
Li Si Fu
Keyword(s):  
Computer Vision ◽  
Computer Technology ◽  
Agricultural Production ◽  
Agricultural Development ◽  
Rapid Development ◽  
Agricultural Field ◽  
Crop Monitoring ◽  
Computer Vision Technology ◽  
Crop Disease

Computer vision technology has been widely applied to various fields of agricultural development, and with the rapid development of computer technology, graphics and image processing technology, enormous progress has been achieved on its applications in agriculture. This paper has reviewed and summarized the development of computer vision technology mainly in detection and grading of the quality of agricultural products, crop monitoring, automation of agricultural production, crop disease identification and other aspects and has discussed the prospect for future development.

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.

scholarly journals Plant Diseases Detection and Pesticide Suggestion Implementation

2021 ◽  
Vol 9 (VI) ◽  
pp. 1806-1812
Author(s):  
Onkar Kunjir
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Transfer Learning ◽  
Plant Disease ◽  
Plant Diseases ◽  
Disease Detection ◽  
Plant Leaf

Plant diseases affect the life of not only farmers but also businesses which are dependent on it. Plant disease detection is a computer vision problem which tries to identify the disease splat is infected using an image of a plant leaf. Different kinds of models have been proposed to tackle this problem. This paper focuses on generating small, lightweight and accurate models with the help of deep learning and transfer learning.

scholarly journals Experimental study on crop disease detection based on deep learning

2019 ◽  
Vol 569 ◽  
pp. 052034
Author(s):  
Xiaoling Xia ◽  
Yongbo Wu ◽  
Qinyang Lu ◽  
Changqi Fan
Keyword(s):  
Experimental Study ◽  
Deep Learning ◽  
Disease Detection ◽  
Crop Disease

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 A Deep Learning Enabled Multi-Class Plant Disease Detection Model Based on Computer Vision

AI ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 413-428
Author(s):  
Arunabha M. Roy ◽  
Jayabrata Bhaduri
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Large Scale ◽  
Plant Disease ◽  
State Of The Art ◽  
Plant Diseases ◽  
Disease Detection ◽  
Detection Model ◽  
Proposed Model ◽  
Apple Plant

In this paper, a deep learning enabled object detection model for multi-class plant disease has been proposed based on a state-of-the-art computer vision algorithm. While most existing models are limited to disease detection on a large scale, the current model addresses the accurate detection of fine-grained, multi-scale early disease detection. The proposed model has been improved to optimize for both detection speed and accuracy and applied to multi-class apple plant disease detection in the real environment. The mean average precision (mAP) and F1-score of the detection model reached up to 91.2% and 95.9%, respectively, at a detection rate of 56.9 FPS. The overall detection result demonstrates that the current algorithm significantly outperforms the state-of-the-art detection model with a 9.05% increase in precision and 7.6% increase in F1-score. The proposed model can be employed as an effective and efficient method to detect different apple plant diseases under complex orchard scenarios.

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

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