Ripeness Detection of Areca Nut Using GoogLe Net

Areca nut cultivation is a significant commercial crop in India, with the country ranking second globally in production and land area. Harvesting areca nuts at optimal stages is critical for yield, typically requiring a skilled tree climber and a ripeness assessor. The current method involves manually plucking a sample nut and testing its ripeness by peeling it with teeth, guiding the climber on whether to cut the bunch. However, inaccurate predictions can lead to substantial crop losses. This research develops an automated system to accurately predict areca nut ripeness using a web camera, Raspberry Pi, and cutter unit. The system analyzes the ripe stages of areca by feature extraction using GoogLe Net, focusing on color, texture, shape, edges, and corners to classify nuts into unripe, intermediate, and ripe stages. Key findings indicate that the automated system significantly improves accuracy in determining nut ripeness compared to manual methods. This advancement minimizes crop losses and reduces labor demands, offering a more efficient solution for areca nut farmers. Integrating this system with a climbing unit has the potential to revolutionize areca nut farming by addressing the limitations of traditional manual methods.