While much of modern agriculture is based on mass mechanized production, advances in sensing and manipulation technologies may facilitate precision autonomous operations that could improve crop yield and quality while saving energy, reducing manpower, and being environmentally friendly. In this paper, we focus on autonomous spraying in vineyards and present four machine vision algorithms that facilitate selective spraying. In the first set of algorithms we show how statistical measures, learning, and shape matching can be used to detect and localize the grape clusters to guide selected application of hormones to the fruit, but not the foliage. We also present another algorithm for the detection and localization of foliage in order to facilitate precision application of pesticide. All image-processing algorithms were tested on data from movies acquired in vineyards during the growing season of 2008 and their evaluation includes analyses of the potential pesticide and hormone reduction. Results show 90% accuracy of grape cluster detection leading to 30% reduction in the use of pesticides. The database of images is placed on the Internet and available to the public to continue developing the detection algorithms.

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Spraying pesticides is a key element of agriculture worldwide, since 30% to 35% of crop losses can be prevented when harmful insects and diseases are eliminated by applying pesticides. Site‐specific spraying can help reduce pesticide application; however, target detection is limited due to the complex agricultural environment. This paper presents a human‐robot collaborative sprayer designed for site‐specific targeted spraying. The robotic sprayer platform, the framework, and tools for the robotic sprayer to collaborate with a remote human operator for the target detection and spraying tasks are detailed. An experiment to evaluate the elements of the collaborative human‐robot framework working in sync was designed, implemented, and evaluated. The collaborative spraying system shows a 50% reduction of sprayed material. The experiment also proves the feasibility of human‐robot collaboration for the complex task of spraying specific targets considering both the True Positive (TP) and False Positive (FP) rates.

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