The Oyster Mushroom Harvesting Determination System Based On Image Processing and Multi Layer Perceptron

Oyster mushroom cultivation in Indonesia has seen rapid growth in recent years, particularly in South Sulawesi. The demand for oyster mushrooms is increasing as they are considered a nutritious food source. However, mushroom farmers are currently unable to fulfill market demand due to limited harvest yields. The primary factor contributing to this issue is the farmers' lack of skills in oyster mushroom cultivation. Therefore, an intelligent system is needed to identify and monitor the growth of oyster mushrooms, which can help to improve harvest yields. In this research, a system for determining oyster mushroom harvest timing will be designed using image processing techniques. This system will work by analyzing images of oyster mushrooms captured using a digital camera on the mushroom growing medium and then identifying visual characteristics that indicate mushroom maturity, such as color, texture, and size. The proposed method consists of several stages: image dataset collection, image preprocessing, image segmentation, morphological operations, feature extraction, and image classification based on Multi-Layer Perceptron (MLP). The dataset obtained includes 150 images of oyster mushrooms, divided into two classes: ready for harvest and not ready for harvest. The test results show that the proposed method can accurately identify oyster mushrooms as either ready for harvest or not. The classification model achieved an accuracy rate of 96.67%. By utilizing this technology, it is expected to enhance efficiency and consistency in the harvesting process and assist farmers in making informed decisions.