WeedOut: An Autonomous Weed Sprayer in Smart Agriculture Framework Using Semi-Supervised Non-CNN Annotation

With rising challenges and depleting resources, many automation solutions have been developed in agriculture. Integration of Internet-of-Agro-Things (IoAT) and Artificial Intelligence (AI) helped gain better yields while maximizing utilization of minimal resources. Weed management being a task affecting quality and yield of crop attracted attention of automation. However, due to the diverse nature of agriculture, same crop from various geographical locations in different growth stages exhibit different features. Additionally, unknown weeds might also exist in the farm rendering feature based supervised CNN solutions not suitable for weed classification. The current paper presents a weed management Agriculture Cyber-Physical System (A-CPS) called WeedOut with a novel methodology enabling it to work in feature variant environments. WeedOut uses a Semi-Supervised methodology that classifies crops by their shapes and labels them as primary crop and weed crop with minimal inputs from farmer. An autonomous weed sprayer uses outputted labeled images to spray herbicide at weed locations and save primary crop.