MobileNet Backbone Based Approach for Quality Classification of Straw Mushrooms (Volvariella volvacea) Using Convolutional Neural Networks (CNN)

Straw mushrooms (Volvariella volvacea) are a crucial commodity in Indonesia, with consumption on the rise due to their nutritional value and increasing demand for healthy food options. Despite this growth, farmers often struggle with accurately assessing the post-harvest quality of mushrooms according to market standards, which can diminish their economic value. Manual classification, which relies on human judgment and estimation, is frequently inefficient and susceptible to errors such as inconsistencies in quality assessment and limitations in detecting subtle variations. This study aims to automate the classification of straw mushrooms based on quality using deep learning, specifically by employing MobileNetv3 as the backbone for classifying mushrooms based on their shape and color by the Indonesian National Standards (SNI). The MobileNet-CNN Backbone model implemented in this study demonstrated exceptional performance, achieving a classification accuracy of 99%, thus proving its effectiveness and reliability in replacing traditional manual methods. The results of this research indicate significant potential for applying deep learning models to enhance the efficiency and precision of mushroom quality assessment. However, there remain challenges that require further development, including adding more diverse background data, improving image resolution, and refining data augmentation techniques. Addressing these challenges is essential for achieving optimal results in varying environmental conditions, ensuring the model can be broadly implemented in the agricultural industry. Such advancements could lead to more consistent and accurate quality assessments, benefiting producers and consumers in the mushroom market.