<![CDATA[Banana ripeness is an important factor that determines the quality, taste and shelf life of the fruit. Manually determining maturity levels tends to be subjective and inconsistent, so a more accurate and efficient automatic system is needed. This research conducted a SLR to evaluate image processing and machine learning techniques in banana ripeness classification CNN is proven to be the most dominant and effective method, with significant accuracy results. Other methods such as kNN, Fuzzy Logic, and ANN also show great potential. The main challenges in developing classification models include image data variability, dataset limitations, and hardware limitations. Recent trends include the use of HSI and multimodal approaches to improve accuracy. Suggestions for future research include collecting larger and more diverse datasets, using data augmentation techniques, exploring HSI sensing, and validating models under real conditions. Thus, this research is expected to make a significant contribution in the development of an automatic system for banana ripeness classification, which can be applied in the agricultural and food industries.]]>
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