Abstract:
"Coconuts hold significant cultural and economic importance in Sri Lanka, with their widespread consumption motivating locals to cultivate and earn a living from this tropical crop. The country's favorable weather makes it an ideal location for growing coconut trees. To maximize yields from each tree, efficient and timely harvesting is essential. Cultivators must possess expertise in determining coconut maturity to ensure optimal harvest timing, a critical factor given the potential challenges in distinguishing mature bunches.
Sri Lanka, renowned for coconut cultivation, faces the challenge of skilled labor shortages. This study addresses this concern by developing an automated system using image processing techniques to detect and identify mature and immature coconut bunches. By analyzing user-supplied images, the system eliminates the need for domain experts, streamlining the harvesting process.
The research focuses on creating a convolutional neural network with the goal of extracting coconut features and aiding in the identification of maturity stages. This technological approach aims to enhance efficiency, ensuring that coconuts are harvested at their peak maturity, preventing the harvesting of immature coconuts or the risk of coconuts falling from trees due to overripening.
As coconuts, scientifically known as Coccus nucifera, rank among Sri Lanka's top three export crops, the success of this research not only benefits local cultivators but also contributes to the country's economic prosperity. The automated system, driven by advanced technology, seeks to revolutionize coconut harvesting practices, making them more precise, efficient, and accessible to a broader audience."
