Artificial Intelligent IoT-Based Cognitive Hardware for Agricultural Precision Analysis

Traditionally, farmers have used human resources for productivity, which could be more efficient and manageable. Farmers have been trying to improve agricultural efficiency and optimize productivity with limited cultivation resources. This study presents an intelligent circulating agricultural farming system that implements monitoring, alerting, automation, and environmental prediction functions. With various sensors, the system constantly collects data on climate conditions, including 1) temperature, 2) humidity, and 3) soil content. Furthermore, we integrated machine learning to forecast the requirements for temperature, humidity, and fertilizer, the most significant growth factors for planting. As a result, the proposed system successfully controlled the cultivation more precisely. An extensive experimentation was conducted on specific crops and environmental conditions to evaluate the proposed model's efficacy. The findings of this research contribute to a deeper understanding of the potential benefits of the proposed integrated system. The results demonstrate how the AI, IoT, and cognitive hardware framework can significantly enhance agricultural precision, ultimately leading to more sustainable and efficient crop production practices.