Enhancing Agricultural Sustainability Through AI-Powered Image Processing: Review Study on Plant Disease Detection - Meena Jindal,; Khushwant Kaur, (International Journal of Scientific Research in Science and Technology, 2024)
The agricultural field is encountering multiple problems with the climate changing, population booming and overusing chemical pesticide which all lead to unsustainable agriculture. Affecting quality and yield, plant diseases account for a heavy loss from the final production. Conventional plant disease detection is definitely the aforementioned matter as well, profound education analyzing with labor-intensive and time-consuming procedure yet not so accurate. By merging artificial intelligence (AI) with image processing, plant disease diagnosis can be automated quickly and efficiently. It uses machine learning algorithms, combined with high-resolution imagery to detect disease symptoms in the early stage of infestation thereby making the treatment process largely dependent on chemical control. In this paper, we reviewed state-of-the-art methods which have experience significant improvement and development in terms of image processing approaches using AI for plant disease recognition. We made a lot of progress however there are still many gaps to fill like other data types, real-time processing and generalizability models that need to be incorporated with farming practices as well accessibility considering all the factors is important for economic viability. Overcoming these gaps requires a holistic approach by combining AI innovations with perspectives from the fields of agronomy and agricultural economics. Future research could potentially concentrate in improving the real-time process, increasing model interpretability and integration with current agricultural systems. Overcoming these challenges, AI-powered image processing can be the backbone of precision agriculture that could secure our food supply and make farming more sustainable.
Keywords: AI; image processing; plant disease detection; sustainable agriculture; precision agriculture; machine learning; early intervention; food security