Determination of Zea Mays Plant Fertility Level in Automatic Fodder Systems using Supervised Learning based on GLCM and Physical Feature
Received: 2 December 2024 | Revised: 2 January 2025 | Accepted: 5 January 2025 | Online: 2 February 2025
Corresponding author: Haryanto
Abstract
The problem during the dry season is the availability of animal feed, especially for cattle. One of the efforts made is to use fermented feed and corn fodder. Automated feedstock monitoring and control is one of the technologies that has been developed. This study proposes a method to determine the fertility of Zea May sp plants in automatic fodder using supervised learning based on Self-Organizing Map (SOM), Gray Level Co-occurrence Matrix (GLCM), and physical features. The results showed that the system worked satisfactorily, where both methods achieved an accuracy of 93.5% on 3-day Zea Mays fodder using SOM and the highest on 12-day Zea Mays fodder using both methods with an accuracy of 96%. Although this system has shown good performance using both SOM and K-means, in some conditions, K-means achieved higher performance. These contributions are expected to help farmers provide animal feed.
Keywords:
SOM, k Means, zea mays, Beluga Whale Optimizer (BWO), GLCMDownloads
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