scholarly journals Analysis of Tomato Leaf Disease Identification Techniques

2021 ◽  
Vol 2 (2) ◽  
pp. 98-103
Author(s):  
Gaurav Chopra ◽  
Pawan Whig
Keyword(s):  
Learning Algorithm ◽  
State Of The Art ◽  
Tomato Leaf ◽  
Plant Diseases ◽  
Disease Classification ◽  
Identification System ◽  
Great Success ◽  
Tomato Crop ◽  
Leaf Disease ◽  
Comparable Accuracy

India loses thousands of metric tons of tomato crop every year due to pests and diseases. Tomato leaf disease is a major issue that causes significant losses to farmers and possess a threat to the agriculture sector. Understanding how does an algorithm learn to classify different types of tomato leaf disease will help scientist and engineers built accurate models for tomato leaf disease detection. Convolutional neural networks with backpropagation algorithms have achieved great success in diagnosing various plant diseases. However, human benchmarks in diagnosing plant disease have still not been displayed by any computer vision method. Under different conditions, the accuracy of the plant identification system is much lower than expected by algorithms. This study performs analysis on features learned by the backpropagation algorithm and studies the state-of-the-art results achieved by image-based classification methods. The analysis is shown through gradient-based visualization methods. In our analysis, the most descriptive approach to generated attention maps is Grad-CAM. Moreover, it is also shown that using a different learning algorithm than backpropagation is also possible to achieve comparable accuracy to that of deep learning models. Hence, state-of-the-art results might show that Convolutional Neural Network achieves human comparable accuracy in tomato leaf disease classification through supervised learning. But, both genetic algorithms and semi-supervised models hold the potential to built precise systems for tomato leaf detection.

scholarly journals Automatic Tomato Plant Leaf Disease Classification using Multi-Kernel Support Vector Machine

2020 ◽  
Vol 9 (5) ◽  
pp. 560-565
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithm ◽  
Early Stage ◽  
Region Of Interest ◽  
Input Image ◽  
Disease Classification ◽  
Support Vector ◽  
Leaf Disease ◽  
Kernel Support Vector Machine

In agriculture the major problem is leaf disease identifying these disease in early stage increases the yield. To reduce the loss identifying the various disease is very important. In this work , an efficient technique for identifying unhealthy tomato leaves using a machine learning algorithm is proposed. Support Vector Machines (SVM) is the methodology of machine learning , and have been successfully applied to a number of applications to identify region of interest, classify the region. The proposed algorithm has three main staggers, namely preprocessing, feature extraction and classification. In preprocessing, the images are converted to RGB and the average filter is used to eliminate the noise in the input image. After the pre-processing stage, features such as texture, color and shape are extracted from each image. Then, the extracted features are presented to the classifier to classify an input tomato leaf as a healthy or unhealthy image. For classification, in this paper, a multi-kernel support vector machine (MKSVM) is used. The performance of the proposed method is analysed on the basis of different metrics, such as accuracy, sensitivity and specificity. The images used in the test are collected from the plant village. The proposed method implemented in MATLAB.

scholarly journals Design and Implementation of an Efficient Rose Leaf Disease Detection using K-Nearest Neighbours

2020 ◽  
Vol 9 (3) ◽  
pp. 21-27
Keyword(s):  
Plant Disease ◽  
Spot Size ◽  
Extraction Process ◽  
Disease Classification ◽  
Identification System ◽  
Automated Identification ◽  
Accurate Identification ◽  
Leaf Disease ◽  
Key Factor ◽  
Agriculture Products

Plants are prone to different diseases caused by multiple reasons like environmental conditions, light, bacteria, and fungus. These diseases always have some physical characteristics on the leaves, stems, and fruit, such as changes in natural appearance, spot, size, etc. Due to similar patterns, distinguishing and identifying category of plant disease is the most challenging task. Therefore, efficient and flawless mechanisms should be discovered earlier so that accurate identification and prevention can be performed to avoid several losses of the entire plant. Therefore, an automated identification system can be a key factor in preventing loss in the cultivation and maintaining high quality of agriculture products. This paper introduces modeling of rose plant leaf disease classification technique using feature extraction process and supervised learning mechanism. The outcome of the proposed study justifies the scope of the proposed system in terms of accuracy towards the classification of different kind of rose plant disease.

scholarly journals Tomato Leaf Disease Detection using Back Propagation Neural Network

2020 ◽  
Vol 9 (8) ◽  
pp. 529-538
Keyword(s):  
Neural Network ◽  
Traditional Approach ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Plant Diseases ◽  
Disease Detection ◽  
Large Drop ◽  
Tomato Crop ◽  
Leaf Disease ◽  
Area Of Interest

Most of the Indian economy rely on agriculture, so identifying any diseases crop in early stages is very crucial as these diseases in plants causes a large drop in the production and economy of the farmers and therefore, degradation of the crop which emphasize on the early detection of the plant disease. These days, detection of plant diseases has become a hot topic in the area of interest of the researchers. Farmers followed a traditional approach for identifying and detecting diseases in plants with naked eyes, which didn’t help much as the disease may have caused much damage to the plant. Tomato crop shares a huge portion of Indian cuisine and can be prone to various Air-Bourne and Soil-Bourne diseases. In this paper, we tried to automate the Tomato Plant Leaf disease detection by studying the various features of diseased and healthy leaves. The technique used is pattern recognition using Back-Propagation Neural network and comparing the results of this neural network on different features set. Several steps included are image acquisition, image pre-processing, features extraction, subset creation and BPNN classification.

scholarly journals Regularized Evolution for Image Classifier Architecture Search

2019 ◽  
Vol 33 ◽  
pp. 4780-4789 ◽  
Author(s):  
Esteban Real ◽  
Alok Aggarwal ◽  
Yanping Huang ◽  
Quoc V. Le
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Simple Method ◽  
Current State ◽  
Network Topologies ◽  
Comparable Accuracy ◽  
Tournament Selection ◽  
Complex Architecture ◽  
First Time

The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically. Although evolutionary algorithms have been repeatedly applied to neural network topologies, the image classifiers thus discovered have remained inferior to human-crafted ones. Here, we evolve an image classifier— AmoebaNet-A—that surpasses hand-designs for the first time. To do this, we modify the tournament selection evolutionary algorithm by introducing an age property to favor the younger genotypes. Matching size, AmoebaNet-A has comparable accuracy to current state-of-the-art ImageNet models discovered with more complex architecture-search methods. Scaled to larger size, AmoebaNet-A sets a new state-of-theart 83.9% top-1 / 96.6% top-5 ImageNet accuracy. In a controlled comparison against a well known reinforcement learning algorithm, we give evidence that evolution can obtain results faster with the same hardware, especially at the earlier stages of the search. This is relevant when fewer compute resources are available. Evolution is, thus, a simple method to effectively discover high-quality architectures.

scholarly journals RICE PLANT DISEASE CLASSIFICATION USING TRANSFER LEARNING OF DEEP CONVOLUTION NEURAL NETWORK

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 631-635 ◽  
Author(s):  
V. K. Shrivastava ◽  
M. K. Pradhan ◽  
S. Minz ◽  
M. P. Thakur
Keyword(s):  
Neural Network ◽  
Rice Plant ◽  
Plant Disease ◽  
Crop Protection ◽  
Visual Observation ◽  
Plant Diseases ◽  
Disease Classification ◽  
Real Field ◽  
Support Vector ◽  
Identification System

<p><strong>Abstract.</strong> Early and accurate diagnosis of plant diseases is a vital step in the crop protection system. In traditional practices, identification is performed either by visual observation or by testing in laboratory. The visual observation requires expertise and it may vary subject to an individual which may lead to an error while the laboratory test is time consuming and may not be able to provide the results in time. To overcome these issues, image based machine learning approach to detect and classify plant diseases has been presented in literature. We have focused specifically on rice plant (<i>Oryza sativa</i>) disease in this paper. The images of the diseased symptoms in leaves and stems have been captured from the rice field. We have collected a total of 619 rice plant diseased images from the real field condition belong to four classes:(a) Rice Blast (RB), (b) Bacterial Leaf Blight (BLB), (c) Sheat Blight (SB) and (d) Healthy Leave (HL). We have used a pre-trained deep convolutional neural network(CNN) as a feature extractor and Support Vector Machine (SVM) as a classifier. We have obtained encouraging results. The early identification of rice diseases by this approach could be used as a preventive measure well as an early warning system. Further, it could be extended to develop a rice plant disease identification system on real agriculture field.</p>

scholarly journals Tomato leaf disease classification by exploiting transfer learning and feature concatenation

2022 ◽  
Author(s):  
Mehdhar S. A. M. Al‐gaashani ◽  
Fengjun Shang ◽  
Mohammed S. A. Muthanna ◽  
Mashael Khayyat ◽  
Ahmed A. Abd El‐Latif
Keyword(s):  
Transfer Learning ◽  
Tomato Leaf ◽  
Disease Classification ◽  
Leaf Disease

Performance Analysis of Deep Neural Networks for Tomato Leaf Disease Classification with Server-Based Computing

2021 ◽  
Author(s):  
Kartik E. Cholachgudda ◽  
Rajashekhar C. Biradar ◽  
Kouame Yann Olivier Akansie ◽  
R. Lohith ◽  
Aras Amruth.Raj Purushotham
Keyword(s):  
Neural Networks ◽  
Performance Analysis ◽  
Deep Neural Networks ◽  
Tomato Leaf ◽  
Disease Classification ◽  
Leaf Disease
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