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 Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Srdjan Sladojevic ◽  
Marko Arsenovic ◽  
Andras Anderla ◽  
Dubravko Culibrk ◽  
Darko Stefanovic
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Plant Disease ◽  
Plant Diseases ◽  
New Approach ◽  
Recognition Model ◽  
Convolutional Networks ◽  
Learning Framework ◽  
First Time ◽  
Leaf Image

The latest generation of convolutional 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 plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. 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. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

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 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 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 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 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.

Plant Disease Detection Using Machine Learning Approaches

2021 ◽  
pp. 122-130
Author(s):  
Sukanta Ghosh ◽  
Shubhanshu Arya ◽  
Amar Singh
Keyword(s):  
Plant Disease ◽  
Crop Yields ◽  
Detection System ◽  
Visual Observation ◽  
Plant Diseases ◽  
Disease Detection ◽  
Learning Approaches ◽  
Factors Affecting ◽  
Plant Leaf ◽  
Market Situation

Agricultural production is one of the main factors affecting a country's domestic market situation. Many problems are the reasons for estimating crop yields, which vary in different parts of the world. Overuse of chemical fertilizers, uneven distribution of rainfall, and uneven soil fertility lead to plant diseases. This forces us to focus on effective methods for detecting plant diseases. It is important to find an effective plant disease detection technique. Plants need to be monitored from the beginning of their life cycle to avoid such diseases. Observation is a kind of visual observation, which is time-consuming, costly, and requires a lot of experience. For speeding up this process, it is necessary to automate the disease detection system. A lot of researchers have developed plant leaf detection systems based on various technologies. In this chapter, the authors discuss the potential of methods for detecting plant leaf diseases. It includes various steps such as image acquisition, image segmentation, feature extraction, and classification.

scholarly journals Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 939 ◽  
Author(s):  
Marko Arsenovic ◽  
Mirjana Karanovic ◽  
Srdjan Sladojevic ◽  
Andras Anderla ◽  
Darko Stefanovic
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Real Life ◽  
Weather Conditions ◽  
Plant Diseases ◽  
Disease Classification ◽  
Generative Adversarial Networks ◽  
Disease Detection ◽  
Single Leaf ◽  
The Impact

Plant diseases cause great damage in agriculture, resulting in significant yield losses. The recent expansion of deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. The current limitations and shortcomings of existing plant disease detection models are presented and discussed in this paper. Furthermore, a new dataset containing 79,265 images was introduced with the aim to become the largest dataset containing leaf images. Images were taken in various weather conditions, at different angles, and daylight hours with an inconsistent background mimicking practical situations. Two approaches were used to augment the number of images in the dataset: traditional augmentation methods and state-of-the-art style generative adversarial networks. Several experiments were conducted to test the impact of training in a controlled environment and usage in real-life situations to accurately identify plant diseases in a complex background and in various conditions including the detection of multiple diseases in a single leaf. Finally, a novel two-stage architecture of a neural network was proposed for plant disease classification focused on a real environment. The trained model achieved an accuracy of 93.67%.

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