关键词: 遥感, 蓝莓, 收获, 智慧农业, 对象识别

Abstract: During the blueberry harvesting season, the pre-identification of fruit ripeness is of great importance for harvest management and yield assessment. The hyperspectral (HS) imaging technology based on remote sensing which contains abundance of spectral and spatial information, provide great potential for development in target detection from complex background. In this study, HS images of three representative southern highbush blueberry varieties were collected in Citra and Waldo, FL, USA in the 2013 harvesting season. Three maturity stages (mature, intermediate and immature) of blueberry fruit were identified from the images containing different background objects such as leaves, branches, sky, and ground. A joint algorithm (named as ‘SVDD+K’) of blueberry fruit maturity recognition model by support vector data description (SVDD) and K-means clustering was used to discriminate mixed blueberry fruit based on pixels and appearance of different objects. The results showed that the detection accuracies were 96.1% for mature fruit, 94.7% for intermediate fruit and 91.2% for immature fruit. To evaluate the performance of the proposed algorithm, other classification methods, i.e., K-nearest neighbor and spectral angle mapping, were used to compare the results. With the highest detection accuracy, the newly developed SVDD+K algorithm was more adaptable to complex backgrounds, especially for small-size objects, which were more accurate in recognition of small-size objects.

Key words: remote sensing, blueberry, harvesting, smart agriculture, target detection