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Many tasks involved in viticulture are labor intensive. Farmers frequently monitor the vineyard to check grape conditions, damage due to infections from pests and insects, grape growth, and to estimate optimal harvest time. Such monitoring is often done manually by the farmers. Manual monitoring of large vineyards is time and labor consuming process. To this end, robots have a big potential to increase productivity in farms by automating various tasks. We propose a low-cost semantic monitoring system for vineyards using autonomous robots. The system uses inexpensive cameras, processing boards, and sensors to remotely provide timely information to the farmers on their computer and smart phone. Unlike traditional systems, the proposed system logs data ‘semantically’, which enables pin-pointed monitoring of vineyards. In other words, the farmers can monitor only specific areas of the vineyard as desired. The proposed algorithm is robust for occlusions, and intelligently logs image data based on the movement of the robot. The proposed system was tested in actual vineyards with real robots. Due to its compactness and portability, the proposed system can be used as an extension in conjunction with already existing autonomous robot systems used in vineyards. The results show that pin-pointed remote monitoring of desired areas of the vineyard is a very useful and inexpensive tool for the farmers to save a lot of time and labor.

Many tasks involved in viticulture are labor intensive. Farmers frequently monitor the vineyard to check grape conditions, damage due to infections from pests and insects, grape growth, and to estimate optimal harvest time. Such monitoring is often done manually by the farmers. Manual monitoring of large vineyards is time and labor consuming process. To this end, robots have a big potential to increase productivity in farms by automating various tasks. We propose a low-cost semantic monitoring system for vineyards using autonomous robots. The system uses inexpensive cameras, processing boards, and sensors to remotely provide timely information to the farmers on their computer and smart phone. Unlike traditional systems, the proposed system logs data ‘semantically’, which enables pin-pointed monitoring of vineyards. In other words, the farmers can monitor only specific areas of the vineyard as desired. The proposed algorithm is robust for occlusions, and intelligently logs image data based on the movement of the robot. The proposed system was tested in actual vineyards with real robots. Due to its compactness and portability, the proposed system can be used as an extension in conjunction with already existing autonomous robot systems used in vineyards. The results show that pin-pointed remote monitoring of desired areas of the vineyard is a very useful and inexpensive tool for the farmers to save a lot of time and labor.