AI and ML for Identifying Rice Infections in Agro-Business

For the agro-business, rice quality and resistant to diseases are essential. A few conditions must be met for the procedures and strategies to be efficient and successful in raising the harvest output. Among other fields, computer science breakthroughs have contributed to innovation in agriculture. This study has brought attention to the gadgets that use strong AI and machine learning methods. These techniques offer very high results for diagnosing diseases from images from branches, crops, or seeds. In this sense, the research provides a survey that includes a precise agribusiness focus meant to deepen knowledge about rice, a among the main crops in the globe. The review and analysis of several papers that have been published in the previous eight years using various methods for determining the health of seedlings, agricultural diseases, and grain quality are presented in this work. Experiments are carried out for data extraction using the Internet Journal of Sciences and Scopus databases to analyze current research in the topic of rice disease detection using artificial intelligence by using global scrutiny, year-wise and country-wise quotations, and so on. This is done in order to support the various researchers working in this field. Farmers are increasingly turning to machine learning, artificial intelligence, and other techniques to increase the effectiveness of crop management. This involves recognizing crops and shielding them from diseases and pest insects. Intelligent systems, which are situation-adaptive and learning-based, are anticipated to be the most popular methods in the days to come. They increase how well these sorts of situations can be handled. The perfect environment for creating the ecosystem required for smart farming is provided by emerging technologies including artificial intelligence (AI), computer vision, satellite photographing, data analysis, and computer vision.