Prediction of Duration of Dry Bamboo Leaf Counting Using Fuzzy Logic

Abstract

Dry bamboo leaves are selected as a planting medium for ornamental plants to enhance nutrient content and improve soil drainage. Before being processed into fertilizer, dry bamboo leaves need to be shredded first. The leaf counting process for planting ornamental plants in villages still relies on manual methods such as using knives and scissors, which can be time-consuming and less effective. In response to this issue, a device for shredding dry bamboo leaves has been developed to improve efficiency and facilitate the leaf counting process. The innovation created is a household-scale dry bamboo leaf shredder that is more affordable and easy to mobilize due to its compact dimensions and lighter weight. The device is also easy to operate, as the shredding occurs when it is closed, and the process stops when it is opened, ensuring safe use for various users. The manual control of bamboo leaf weight and counting duration results in inconsistent shredding outcomes. Therefore, development has been carried out on the dry bamboo leaf shredder with artificial intelligence capabilities, specifically using fuzzy logic to automate the counting duration based on the size and weight of the bamboo leaves being inputted.