AI-Based Dish Recommender System for Reducing Fruit Waste through Spoilage Detection and Ripeness Assessment

The objective of this study is to develop an application that can assess ripeness and detect spoilage of the fruits and suggest a dish based on ingredients, freshness and condition of the fruits. Ripeness will assess through image processing gas sensor will assess the spoilage. An easy-to-use mobile interface enable the scanning together with the device to determine the fruits condition and efficient food choices. This study used the Agile methodology to develop the system. Agile is essential, and it has an iterative approach that focuses on flexibility, combination, and continuous improvement for the system. The process was split into several sprints, each working on important specific features, followed by testing and evaluation. Regular meetings were done to check the progression, solve problems, and plan next steps for the system for development. This method allowed the researchers to adapt to the changes and to ensure the system was developed effectively and efficiently. The system uses an app to minimize fruit waste by providing real-time spoilage and ripeness assessments, smart dish recommendations, and timely alerts. By processing few user inputs, gas sensor data, and image classification, the system accurately detects spoilage and ripeness data for multiple types of fruit. Based on the assessments, it recommends dishes that maximize the fruit usage before it spoils. The user-friendly system helps promote an improved household management and encourages a more mindful consumption. In conclusion the research developed a system using machine learning, image processing and sensor to assess the ripeness and spoilage of the fruits and recommend applicable dishes. Using agile methodology, the system was iteratively tested and improved to ensure the efficiency of the system. The sensor and mobile provide real-times assessment. The proposed system will assist to waste reduction and mindful consumption of the fruits.