Immature green citrus detection based on colour feature and sum of absolute transformed difference (SATD) using colour images in the citrus grove

Recognition and detection of green immature citrus fruit more accurately and efficiently in groves under natural illumination conditions provides a promising benefit for growers to plan application of nutrients during the fruit maturing stages and estimate their yield and profit prior to the harvesting period. The goal of this study was to develop a robust and fast algorithm to detect and count immature green citrus fruit in individual trees from colour images acquired with different fruit sizes and under various illumination conditions. Adaptive Red and Blue chromatic map (ARB) was created and combined with the Hue image extracted after histogram equalization (HEH). The sum of absolute transformed difference (SATD), a block-matching method, was applied to detect potential fruit pixels. After OR operation of the results obtained from colour and SATD analysis which kept as many fruit pixels as possible, a kernel support vector machine (SVM) classifier was built with same learning sets used for different classification stages to remove false positives based on five selected texture features. The algorithm was evaluated with a set of testing images, and achieved more than 83% recognition accuracy. The proposed method can provide a more efficient way for green citrus identification in a grove using colour images.