Abstract
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.
Highlights
Viticulture, a branch of horticulture, is the cultivation and harvesting of grapes and is carried out in many countries
Vineyard monitoring is a task which is often done frequently to check the growth of grapes and damage
An algorithm to improve the robustness of pillar detection was proposed by detecting the pillar in a larger search space
Summary
Viticulture, a branch of horticulture, is the cultivation and harvesting of grapes and is carried out in many countries. The tasks involved in viticulture include monitoring, irrigation, adding fertilizers, canopy management, controlling pests and diseases, monitoring fruit development and characteristics, deciding the harvesting time, and vine pruning during the winter months. Monitoring fruit development and characteristics is typically carried out frequently over the entire area of the vineyard, and is a labor intensive and time consuming task as it involves visual inspection of the fruit and plants by the farmer. The focus of the proposed work is on visual monitoring using autonomous robots. Wireless sensor network for vineyard monitoring that uses image processing is proposed in [32]. We propose a novel way to semantically label image data in vineyards by detecting the pillars set in the vineyard to support grapes.
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