Efficient Detection of Sugarcane Diseases through Intelligent Approaches: A Review | Asian Journal..

Agriculture is the most important sector in terms of influencing the country's economic growth, and it is intertwined with all four quadrants of society. India's most promising crop is sugarcane. It can be planted as a cash crop or a major crop. Sugarcane is harvested by small to medium-sized rural farmers for the production of brown sugar for animal feed. Sugarcane compounds are used in the sugarcane business to make sugar, bio-electricity, bio-ethanol, and other chemical products. To keep up with the world's rising population, sugarcane yields must be increased. Pests and illnesses have a significant impact on sugarcane productivity. As a result, both farmers and the country incur significant economic losses. As a result, early detection of numerous sugarcane diseases and pest management strategies are required to boost productivity. The inability to identify sugarcane leaf diseases with the naked eye leads to erroneous pesticide control measurements. To boost productivity and quality, an automatic detection and early diagnosis of sugarcane illnesses is required. Image processing algorithms extract features from sugarcane leaves quickly and identify disease types at an early stage. This research presents a survey of several image processing techniques and machine learning approaches for sugarcane diagnosis extraction and rapid assessment. The challenges that underpin computational approaches for assessing sugarcane infections are also highlighted, as well as possible future possibilities.

Please see the link :- https://globalpresshub.com/index.php/AJRRA/article/view/1237