Cotton Leaf Disease Detection by Feature Extraction

Ghaiwat, Savita N.; Bhalotra, Parul S. Arora

doi:10.4018/978-1-4666-8737-0.ch005

Cited by 7 publications

(2 citation statements)

References 2 publications

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“…Due to the small database size problem, a large portion of the data set is used for the training phase in most of the deep learning methods. However, very few exceptions are there, for example [57][58][59]. Furthermore, the available database images are collected in very constrained environmental conditions.…”

Section: Comparison Of Performance and Results Discussionmentioning

confidence: 99%

Image‐Based Detection of Plant Diseases: From Classical Machine Learning to Deep Learning Journey

Khan

Albattah

et al. 2021

Wireless Communications and Mobile Computing

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Plant disease automation in agriculture science is the primary concern for every country, as the food demand is increasing at a fast rate due to an increase in population. Moreover, the increased use of technology today has increased the efficacy and accuracy of detecting diseases in plants and animals. The detection process marks the beginning of a series of activities to fight the diseases and reduce their spread. Some diseases are also transmitted between animals and human beings, making it hard to fight them. For many years, scientists have researched how to deal with the common diseases that affect humans and plants. However, there are still many parts of the detection and discovery process that have not been completed. The technology used in medical procedures has not been adequate to detect all diseases on time, and that is why some diseases turn out to become pandemics because they are hard to detect on time. Our focus is to clarify the details about the diseases and how to detect them promptly with artificial intelligence. We discuss the use of machine learning and deep learning to detect diseases in plants automatically. Our study also focuses on how machine learning methods have been moved from conventional machine learning to deep learning in the last five years. Furthermore, different data sets related to plant diseases are discussed in detail. The challenges and problems associated with the existing systems are also presented.

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Section: Comparison Of Performance and Results Discussionmentioning

confidence: 99%

Image‐Based Detection of Plant Diseases: From Classical Machine Learning to Deep Learning Journey

Khan

Albattah

et al. 2021

Wireless Communications and Mobile Computing

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“…Multiple attributes are obtained from the sub-images individually. BPNN is used to classify the disease with the obtained features [11]. A Leaf spot disease detection system using Radial Basic Function Neural Network technique using 326 sample images from the plant village dataset has been developed.…”

Section: Introductionmentioning

confidence: 99%

Detection and classification of various pest attacks and infection on plants using recursive backpropagation neural network with GA based particle swarm optimization algorithm

Gangadharan

Kumari

Dhanasekaran

et al. 2020

IJEECS

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<span>Machine learning methodologies are commonly used in the field of precession farming. It prospects greatly in the plant safety measure like disease detection and classification of pest attacks. It highly influences the crop production and management. The venture of our system is to produce healthy plantation. The proposed system involves Enhanced Fractal Texture Feature Analysis and Machine Learning methodology for classification. Hence more than ever there is a need for such a tool that combines image processing methodologies and the Neural network concepts and that is supported by huge cloud of structured data which makes the diagnosis part much easier and convenient. The proposed system recognizes and classifies the plant taxonomy and the infection also it measures the percentage of infection. The neural network concept followed in our proposed system is focused on Artificial Neural Network which uses Recursive Back Propagation Neural network to speed up the training process and the weights on ANN is optimized using Genetic Algorithm based Particle Swarm Optimization technique. We have used MATLAB to implement the concept and obtained better accuracy in disease detection and proved to be an efficient method.</span>

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