artificial intelligence approaches for cotton diseases identification: a systematic literature review using biotechnology

Objectivecotton is a prominent fiber that commands the worldwide industrial and agricultural sectors. cotton is a fundamental material used in the creation of textiles. diagnosing the diseases on cotton plants’ leaves soon is essential to prevent them and enhance productivity. tracking cotton leaf illnesses and assessing plant health is challenging for farmers who rely solely on their subjective expertise and knowledge. moreover, artificial neural networks have been proposed to alleviate limitation of traditional methods and can be used to handle nonlinear and complex data, even when the data is imprecise and noisy. agricultural data can be too large and complex to handle through visual analysis or statistical correlations. this has encouraged the use of machine intelligence or artificial intelligence the objective of this study was to diagnose diseases and improve the cultivation of cotton using artificial intelligence (ai) methods.resultsthe study findings indicate that the current automated detection approaches for cotton crop illnesses are still in their early stages of development with biotechnology and artificial intelligence (ai). this review acknowledges the need to develop automated, cost-effective, dependable, precise, and swift diagnostic tools for detecting cotton leaf diseases to enhance output and quality.conclusionsthis paper analyzes the several computational techniques used at different phases of plant-pathogen structures, including image preparation, segmentation, extracting features and selecting, and categorization. the study identified valid future paths and areas for additional exploration. there is a need for innovative, fully automated computer-assisted methods to identify and categorize various illnesses in cotton crops..

artificial intelligence approaches for cotton diseases identification: a systematic literature review using biotechnology

objectivecotton is a prominent fiber that commands the worldwide industrial and agricultural sectors. cotton is a fundamental material used in the creation of textiles. diagnosing the diseases on cotton plants’ leaves soon is essential to prevent them and enhance productivity. tracking cotton leaf illnesses and assessing plant health is challenging for farmers who rely solely on their subjective expertise and knowledge. moreover, artificial neural networks have been proposed to alleviate limitation of traditional methods and can be used to handle nonlinear and complex data, even when the data is imprecise and noisy. agricultural data can be too large and complex to handle through visual analysis or statistical correlations. this has encouraged the use of machine intelligence or artificial intelligence the objective of this study was to diagnose diseases and improve the cultivation of cotton using artificial intelligence (ai) methods.resultsthe study findings indicate that the current automated detection approaches for cotton crop illnesses are still in their early stages of development with biotechnology and artificial intelligence (ai). this review acknowledges the need to develop automated, cost-effective, dependable, precise, and swift diagnostic tools for detecting cotton leaf diseases to enhance output and quality.conclusionsthis paper analyzes the several computational techniques used at different phases of plant-pathogen structures, including image preparation, segmentation, extracting features and selecting, and categorization. the study identified valid future paths and areas for additional exploration. there is a need for innovative, fully automated computer-assisted methods to identify and categorize various illnesses in cotton crops..