An Automated Citrus Disease Detection System Using Hybrid Feature Descriptor

Plant disease is an on-going challenge for the farmers and it has been one of the major threats to the income and the food security. This project is used to classify plant leaf into diseased and healthy leaf,to improve the quality and quantity of agricultural production in the country. The innovative technology that helps in improve the quality and quantity in the agricultural field is the smart farming system. It represented the modern method that provides cost-effective disease detection and deep learning with convolutional neural networks (CNNs) has achieved large successfulness in the categorisation of different plant leaf diseases. CNN reads a really very larger picture in a simple way. CNN nearly utilised to examine visual imagery and are frequently working behind the scenes in image classification. To extract the general features and then classify them under multiple based upon the features detected. This project will help the farmers financially in increasing the production of the crop yield as well as the overall agricultural production. The paper reviews the expected methods of plant leaf disease detection system that facilitates the advancement in agriculture. It includes various phases such as image preprocessing, image classification, feature extraction and detecting healthy or diseased.

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Video-based Automatic Early Parkinson’s Disease Detection System using Biomechanical Features

Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference ◽

10.3850/978-981-14-8593-0_4630-cd ◽

2020 ◽

Author(s):

Changhong Lin ◽

Shaoping Wang

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Detection System ◽

Disease Detection ◽

Biomechanical Features ◽

Early Parkinson’S Disease

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An Efficient and Affordable R-Pi Based Cardiac Disease Detection System

Lecture Notes in Electrical Engineering - Artificial Intelligence Driven Circuits and Systems ◽

10.1007/978-981-16-6940-8_1 ◽

2021 ◽

pp. 1-15

Author(s):

Neha Arora ◽

Biswajit Mishra

Keyword(s):

Cardiac Disease ◽

Detection System ◽

Disease Detection

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Plant Disease Detection Using Machine Learning Approaches

Advances in Medical Technologies and Clinical Practice - Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease ◽

10.4018/978-1-7998-7188-0.ch009 ◽

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.

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