• Abhinav Kumar
• Prashant Raj
• Aman kumar
• Er. Jyoti

Detecting plant diseases at an early stage is crucial for reducing crop loss and ensuring global food security. Traditional methods for identifying plant diseases, such as visual inspections and laboratory analyses, can be slow and often ineffective. In this paper, we delve into the promising use of multispectral imaging (MSI), a cutting-edge technology that captures data across various wavelengths, enabling the detection of early signs of disease in plants even before they become visible to the human eye. By leveraging machine learning algorithms to analyze this multispectral data, we can detect diseases far earlier, allowing farmers to take swift action. This study also addresses the challenges of implementing MSI in real-world agricultural settings and suggests improvements for better applicability. Our findings demonstrate that MSI, when combined with machine learning techniques, has significant potential to enhance farming sustainability by minimizing reliance on chemical treatments and providing timely alerts to farmers.

"Early diseases detection using MultiSpectral Imaging", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.e746-e749, October-2024, Available :http://www.jetir.org/papers/JETIR2410481.pdf

"Early diseases detection using MultiSpectral Imaging", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. ppe746-e749, October-2024, Available at : http://www.jetir.org/papers/JETIR2410481.pdf