Rice Disease Detection Using Artificial Intelligence and Machine Learning Techniques to Improvise Agro-Business

Agro-business is highly dependent on rice quality and its protection from diseases. There are several prerequisites for the procedures and the strategies that are productive and efficient for expanding the harvest yield. The advancement in computer science has supported various domains; agricultural...

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Published inScientific programming Vol. 2022; pp. 1 - 13
Main Authors Aggarwal, Shruti, Suchithra, M., Chandramouli, N., Sarada, Macha, Verma, Amit, Vetrithangam, D., Pant, Bhaskar, Ambachew Adugna, Biruk
Format Journal Article
LanguageEnglish
Published New York Hindawi 24.06.2022
John Wiley & Sons, Inc
Subjects
Agricultural production
Agriculture
Artificial intelligence
Business machines
Domains
Machine learning
Neural networks
Object recognition
Plant diseases
Rice
Seeds
Support vector machines
India
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Summary:Agro-business is highly dependent on rice quality and its protection from diseases. There are several prerequisites for the procedures and the strategies that are productive and efficient for expanding the harvest yield. The advancement in computer science has supported various domains; agricultural innovation is one of them. The apparatuses which utilize the strategies of advanced artificial intelligence and machine learning have been featured in this paper. These techniques attain abnormally productive outcomes for the recognition of infections engrossing the images of leaves, fields of harvest, or seeds. In this context, this work presents a survey that focuses on accuracy agribusiness for expanding the conception of rice, which is one of the main harvests on the planet. In this paper, the overview and examination of various papers distributed in the most recent eight years with various methodologies identified with crop diseases identification, the health of seedlings, and quality of grain have been introduced. Experiments are performed for knowledge extraction using Web of Science and Scopus databases to analyze research trends in the domain of rice disease identification using artificial intelligence using global analysis, year-wise and country-wise citations, and so on to support various researchers working in this domain.
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ISSN:1058-9244
1875-919X
DOI:10.1155/2022/1757888