Automatic grape bunch detection in vineyards for precise yield estimation

Precise yield estimation using image processing techniques has been demonstrated conceptually on a small scale. Expanding these solutions to larger scale applications requires significant computational power, which need to analyze the entirety of all captured image data. However, many images captured for yield estimation in these processes only contain small areas of useful features for analysis. This paper introduces an image processing algorithm combining color and texture information, and the use of a support vector machine, to accelerate fruit detection by isolating useful features in images. Experiments carried out on two varieties of red grapes (Shiraz and Cabernet Sauvignon) demonstrate an accuracy of 87% and recall of 90%. This method is also shown to remove the restriction on the field of view and background, which limited existing methods and is a first step towards precise and reliable yield estimation on a large scale.