"Development and Advantages of an AI-Driven Smart Lighting, Insect Detection and Automatic Spray System for Precision Agriculture"

Abstract

There is no area more difficult for crop improvement and efficiency in the utilization of resources in agriculture than maintaining the environment under this scenario. Traditional agriculture is a matter of the use of relatively broad insecticides and manpower with wide-spectrum inefficiency and ecological damage. This paper presents an AI-driven intelligent lighting system that performs real-time insect detection and books a sprayer to thus automate and optimize an even more mechanized agricultural practice. The CNN-based insect detection module actually correctly classifies with high-precision recall rates of substantially minimized pesticide use. A statistic of the system's performance indicators like detection accuracy at 95%, and reduction to a level of 40% in the used pesticides is a testament to the even better system performance compared to the traditional methods. The smart lighting aspect would employ HPS lamps and provide the best lighting conditions so enhanced photosynthesis further raises crop yield by 25%. The robotic spray system would spray the pesticide only where required to minimize environmental effect and resource wastage. The solution proposed here offers fast, complete, scalable, and environmentally friendly precision farming that the current solution lacks. An innovative contribution to current agricultural practice is introduced through this research. The issues to be resolved in this research are pest control, optimization of resources, and sustainability in the environment. Future development of this research will entail investigating the feasibility of upscaling this solution, incorporating more features that AI can provide and making it highly accessible to both small- and large-scale farmers.