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A Survey on Various Image Processing Techniques and Machine Learning Models to Detect, Quantify and Classify Foliar Plant Disease by Naik, Akruti (author);Thaker, Hetal (author);Vyas, Dhaval (author) is a Earth and Planetary Sciences article available to read on EtoBox.

What is A Survey on Various Image Processing Techniques and Machine Learning Models to Detect, Quantify and Classify Foliar Plant Disease about?

Agriculture is one of the significant factors that drive India's economy. A decrease in the yield of agricultural food crops due to plant diseases results in great loss to the economy of the developing country. Detection of plant disease at an early stage can decrease the chance of loss on the overall economy. Nowadays, ICT (Information And Communication Technology) plays a major role in all sectors including agriculture. Classical agriculture has been reformed using ICT. Farmers are getting the correct information on time. ICT is necessary for agronomy, it may increase productivity using data generation, storage, and analysis. This paper presents a survey of various image processing techniques and machine learning tools to detect, quantify, and classify plant diseases. Methods that explore visible symptoms in leaves and stems were considered. This paper aims on exploring this wide research area and possible scope of further researcher there by looking at various aspects of review such as accuracy, image processing techniques, machine learning models, and plants on which work has been carried out. This survey is likely to be useful to researchers working both on disease detection o

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Author

Naik, Akruti (author);Thaker, Hetal (author);Vyas, Dhaval (author)

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Earth and Planetary Sciences (Physical Sciences)