Plant Disease Detection System for Agricultural Application in Cloud Using CNN

2018 ◽  
Vol 6 (12) ◽  
pp. 246-249
Author(s):  
Raghavendran.S . ◽  
P.Kumar . ◽  
Silambarasan. K
Keyword(s):  
Plant Disease ◽  
Detection System ◽  
Disease Detection ◽  
Agricultural Application

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 Segmentation and Classification of Plant Disease using SVM–PSO in Cloud

2019 ◽  
Vol 9 (1) ◽  
pp. 2242-2247
Keyword(s):  
Plant Disease ◽  
Detection System ◽  
Plant Diseases ◽  
Disease Detection ◽  
Detection Accuracy ◽  
Main Role ◽  
Overall Design ◽  
Segmented Image ◽  
Storage Place

Area of agriculture plant disease detection attracts is very important one, main role is diseases detection. To develop the plant diseases detection, it required to identify arrival of the diseases in the leaf and instruction to the agriculturalists. In this proposed work, a leaf disease detection system (LDDS) based on Otsu segment (OS) is developed to identify and classify the diseases in the set of leaves. Clustering scheme is offered from segmented image of the diseased leaf. Otsu segmentation is measured the size of segmented leaf are uploaded to less storage place. In observing location, the amounts are retrieved as well as the features are extracted from the original segmented image. The enhancement as well as classification is used to SVM based on PSO classifier. The overall design of this paper is LDDS take scan be calculated in terms of system efficiency and it is compared with the existing methods. The result indicates the research technique offers a whole detection accuracy of 90.5% and classification accuracy of 90.4%.

scholarly journals Smart Plant Disease Detection System

2021 ◽  
Vol 6 (4) ◽  
pp. 13-16
Author(s):  
Bhoopendra Joshi ◽  
Abhinav Kumar ◽  
Satyam Kashyap ◽  
Nooruddin Nagdi ◽  
Sukhdarshan Vinayak ◽  
...  
Keyword(s):  
Plant Disease ◽  
Detection System ◽  
Disease Detection

scholarly journals Plant Disease Detection System using Bag of Visual Words

2018 ◽  
Vol 10 (9) ◽  
pp. 57-63 ◽  
Author(s):  
D. Asir Antony Gnana Singh ◽  
◽  
E. Jebamalar Leavline ◽  
A. K. Abirami ◽  
M. Dhivya
Keyword(s):  
Plant Disease ◽  
Detection System ◽  
Disease Detection ◽  
Bag Of Visual Words ◽  
Visual Words

scholarly journals Design of Shallow Neural Network Based Plant Disease Detection System

2021 ◽  
Vol 5 (4) ◽  
pp. 5-9
Author(s):  
Fatai O. Sunmola ◽  
Olaide A. Agbolade
Keyword(s):  
Neural Network ◽  
Alternaria Alternata ◽  
Leaf Spot ◽  
Bacterial Blight ◽  
Plant Disease ◽  
Detection System ◽  
Disease Detection ◽  
Detection Accuracy ◽  
Cercospora Leaf Spot ◽  
Detection Model

— In this work, we proposed the use of a shallow neural network for plant disease detection. The study focuses on four major diseases that are known to attack some of the most cultivated crops globally. The diseases considered include Bacterial Blight, Anthracnose, Cercospora leaf spot and Alternaria Alternata. In developing the disease detection model, K-means algorithm was used for plant segmentation while color co-occurrence method was used for feature analysis. A shallow neural network trained on 145 training samples was used as a classifier. The detection accuracy of 98.34 %, 98.48%, 98.03% and 98.14% were recorded for Bacterial Blight, Anthracnose, Cercospora leaf spot and Alternaria Alternata diseases respectively. The overall detection accuracy of the model is 98.25%.

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