Ripeness Detection of Areca Nut Using VGG-16

Areca nut, a vital commercial crop in India, ranks second globally in production. Harvesting at specific stages is crucial for optimal yield, requiring at least two skilled individ-uals: a tree climber and a ripeness tester. Before harvesting, a sample nut is tested for ripeness by peeling it with teeth to guide the climber. Inaccurate predictions can lead to significant crop losses. This study aims to develop a system using a web camera, Raspberry Pi, and cutter unit to accurately predict nut ripeness based on various features. By classifying nuts into unripe, intermediate, and ripe stages, this automated harvesting system could revolutionize areca nut farming, reducing labor requirements and benefiting the agriculture sector by avoiding the drawbacks of manual methods.