Proceedings ArticleDOI
Rice Blast Disease Detection and Classification Using Machine Learning Algorithm
S. Ramesh
- pp 255-259
TLDR
A machine learning algorithm is proposed to find the symptoms of the disease in the rice plant using images taken from healthy and blast disease affected leaves using an automatic detection system.Abstract:
Rice blast disease is the major problem in all over the world of agriculture sector. The early detection of this disease will prevent the huge economic loss for the farmer. This paper proposes a machine learning algorithm to find the symptoms of the disease in the rice plant. Automatic detection of plant disease is carried out using machine learning algorithm. Images of healthy and blast disease affected leaves are taken for the proposed system. The features are extracted for the healthy and disease affected parts of the rice leaf. The total data set consists of 300 images and divided for training and testing purposes. These images are processed with the proposed method and the leaf is categorized as either infected or healthy. The simulation results provide an accuracy of 99% for the blast infected images and 100% for the normal images during the training phase. The testing phase accuracy is found to be 90% and 86% for the infected and healthy images respectively.read more
Citations
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Journal ArticleDOI
Machine Learning and Deep Learning Based Computational Techniques in Automatic Agricultural Diseases Detection: Methodologies, Applications, and Challenges
TL;DR: A thorough investigation has been performed to evaluate the possibility of using Machine Learning models to identify plant diseases and various challenges in the use of machine learning and deep learning for plant disease detection and future research directions are enumerated and presented.
Journal ArticleDOI
Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model
Ghazanfar Latif,Sherif Elmeligy Abdelhamid,Roxane Elias Mallouhy,Jaafar Alghazo,Z. A. Kazimi +4 more
TL;DR: This paper proposes a Deep Convolutional Neural Network transfer learning-based approach for the accurate detection and classification of rice leaf disease and achieves significantly better results compared with similar approaches using the same dataset or similar-size datasets reported in the extant literature.
Proceedings ArticleDOI
Smartphone Application for Deep Learning-Based Rice Plant Disease Detection
TL;DR: The results showed that the smartphone-based rice plant disease detection application functioned well, which was able to detect diseases in rice plants and improve the test accuracy value by adding the number of datasets and increasing the quality of the dataset.
Proceedings ArticleDOI
Smart Agriculture Based on IoT and Machine Learning
Geo Abraham,R Raksha,M. Nithya +2 more
TL;DR: In this article, an IoT based prototype system for surveillance is proposed that embeds the concept of multi-class classification technique using Machine and Deep Learning for the labels clear farm, horse, cow, wild elephant and wild boar.
Proceedings ArticleDOI
An IoT based System with Edge Intelligence for Rice Leaf Disease Detection using Machine Learning
TL;DR: In this paper, a rice leaf disease detection system using a lightweight Artificial Intelligent technique using a Raspberry Pi has been presented, which is based on the edge computing concept and achieved 97.50% accuracy.
References
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Book ChapterDOI
Brief Introduction of Back Propagation (BP) Neural Network Algorithm and Its Improvement
TL;DR: This paper focuses on the analysis of the characteristics and mathematical theory of BP neural network and also points out the shortcomings of BP algorithm as well as several methods for improvement.
Proceedings ArticleDOI
Image processing for smart farming: Detection of disease and fruit grading
TL;DR: Effective algorithms for spread of disease and mango counting are demonstrated and artificial neural network concept is used for practical implementation using MATLAB.
Proceedings ArticleDOI
Machine learning regression technique for cotton leaf disease detection and controlling using IoT
TL;DR: In this paper, a Support Vector Machine based regression system for identification and classification of five cotton leaf diseases (Bacterial Blight, Alternaria, Gray Mildew, Cereospra, and Fusarium wilt) is proposed.
Proceedings ArticleDOI
Computer vision based approach to detect rice leaf diseases using texture and color descriptors
TL;DR: A computer vision based automatic system for the diagnosis of diseases caused by pests in the rice plants using genetic algorithm based feature selection approach and Artificial neural network and support vector machine is used for classification.
Proceedings ArticleDOI
Extraction of the Rice Leaf Disease Image Based on BP Neural Network
Libo Liu,Guomin Zhou +1 more
TL;DR: The result shows that the scheme is feasible to identify rice brown spot using image analysis and BP neural network classifier and the design was designed for classifying the healthy and diseased parts of rice leaves.