Recognition of Plant Diseases by Leaf Image Classification Based on Improved AlexNet

2021 ◽  
Author(s):  
Kanakam Soujanya ◽  
J. Jabez
Keyword(s):  
Image Classification ◽  
Plant Diseases ◽  
Leaf Image

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 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.

Disease Identification and Grading of Pomegranate Leaves Using Image Processing and Fuzzy Logic

2013 ◽  
Vol 9 (4) ◽  
pp. 467-479 ◽  
Author(s):  
Sanjeev S. Sannakki ◽  
Vijay S. Rajpurohit ◽  
V. B. Nargund ◽  
R. Arunkumar
Keyword(s):  
Image Processing ◽  
Plant Diseases ◽  
Automated System ◽  
Database Analysis ◽  
Query Image ◽  
Horticultural Crops ◽  
Disease Identification ◽  
Proper Diagnosis ◽  
Processing Steps ◽  
Leaf Image

AbstractPlant diseases cause major losses to several agricultural and horticultural crops around the World. Therefore, methods for proper diagnosis of diseases found in any parts of the plant body play a crucial role in disease management. In the past few decades, many methods and techniques of image processing and soft computing are applied on a number of plants to diagnose and treat variety of plant diseases. Hence, the present work is aimed to develop an automated system that results in three major outcomes for a leaf image. They are disease identification, disease grading and treatment advisory. The methodology begins with capturing of samples of healthy and diseased leaf images of Pomegranate plant. All the images are made to undergo pre-processing steps and different features are extracted and stored in the database. Analysis is done on the extracted features to determine those features that constitute a disease in the leaf. Later, a query image is taken and is tested to determine whether that image is healthy or diseased one. If the query image is found to be diseased, then the grade of the disease is determined. Finally, a treatment advisory module is built which ultimately helps agriculturists/farmers.

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