Published Paper Details:

The goal of this research was to make an assessment of where AI and ML have been applied so far in agriculture and to make some educated speculations about where the field might go from here. This research takes a systematic literature review approach to the articles already published across a variety of scientific platforms that explore the overlap between artificial intelligence (AI) and supply chain management (SC) and machine learning (ML) in the food industry (FT). In this work, we propose a strategy for decreasing agricultural risks and bolstering smart farming practises. The wireless AI-based sensor will raise the bar for intelligent agriculture, which has seen progress in the past. In order to improve prediction in its current form, this research uses image-processing based machine learning techniques. Detecting and controlling cotton leaf diseases is the main objective of this study. The study covers a wide range of topics, including the detection of diseases on leaves, a server-based remote monitoring system, moisture and temperature sensing, and soil sensing. Plant illnesses caused by insects and other pathogens can drastically cut harvest yields if not treated promptly. In this study, we describe a system for assessing soil quality and warding against diseases that affect cotton leaves. Artificial intelligence's regression technique is used by the proposed system to detect and categorise leaf rots. After the virus