This study introduces a mobile application designed to assist visually impaired persons (VIPs) in navigating complex environments, such as supermarkets. Recent assistive tools often identify objects in isolation without providing contextual awareness. In contrast, our proposed system uses seamless scene and object recognition to help users efficiently locate target items and understand their surroundings. Employing a “human-in-the-loop approach”, users control their smartphone camera direction to explore the space. Experiments conducted in a simulated shopping environment show that the system enhances object-finding efficiency and improves user orientation. This approach not only increases independence, but also promotes inclusivity by enabling VIPs to perform everyday tasks with greater confidence and autonomy.

To alleviate social problems in agriculture such as aging and labor force shortages, automatic growth monitoring based on image measurement has been introduced to tomato cultivation in greenhouses. The overlap of leaves and fruits makes precise observations challenging. In this study, we applied context recognition to tomato growth monitoring by using a Bayesian network. The proposed method combines image recognition using convolutional networks and context recognition using Bayesian networks. It enables not only the recognition of individual tomatoes but also the evaluation of tomato plants. An accurate number of tomatoes and the condition of the stocks can be estimated based on the number of ripe and unripened tomatoes in addition to their density information. The verification experiments clarified that a more accurate number of tomatoes could be estimated than with simple tomato detection, and the stock states could also be evaluated correctly. Compared to conventional methods, the method used in this study has improved tomato decision accuracy by 23%.