Revolutionizing Agricultural Harvesting with IoT Application

Abstract

Traditional agriculture labor is characterized by its labor-intensive nature, relying heavily on manual labor. This approach is time-consuming and often results in low yields. Manual labor is susceptible to errors, leading to compromised crop quality. Moreover, the traditional method incurs high costs due to the need to hire numerous workers. Overall, traditional agriculture labor is inefficient, costly, and prone to quality issues, highlighting the need for more efficient and modernized agricultural practices. The current harvesting process for leafy greens relies heavily on manual cutting and individual knotting of each bunch, presenting significant challenges in terms of efficiency, labor shortage, and consistency in quality control. This labor-intensive methodology not only consumes valuable time but also strains increased operational inefficiencies. In this study, we address these challenges by proposing an innovative and automated harvesting system tailored for leafy greens. By integrating cutting-edge robotics and advanced sensing technologies, our solution aims to streamline the harvesting process, mitigating labor shortages, and reducing workforce strain. The system's intelligent automation ensures uniformity in bunching, enhancing both efficiency and the quality of the harvested produce. Through this research, we anticipate a transformative shift in the leafy greens harvesting industry, fostering increased productivity, labor optimization, and improved overall operational performance.

Keywords

• agriculture
• iot
• machines
• knotting
• griming
• harvesting