Accurate recognition of green fruit targets is one of the key technologies for fruit growth monitoring and yield estimation. To solve the problem of fruit misidentification due to the similarity between fruit skin and leaf colors, a progressive detection method of green apples in natural environments was proposed. Image enhancement based on fuzzy set theory was carried out to make the fruit targets more salient in the whole image. Then, the fruit areas were roughly determined by the attention-based information maximization (AIM) algorithm, and the recognized apple regions were cropped according to the adaptive pixel-extending method to remove the background information. After that, accurate segmentation of fruit targets was accomplished by fusing the illumination-invariant image and R-component of the cropped image. To evaluate the performance of this method, it was compared with the illumination invariance theory-based algorithm, mean shift algorithm, K-means clustering algorithm, manifold ranking algorithm and GrabCut algorithm. The test was conducted using 200 green apple images under different growth statuses. Experimental results showed that the segmentation rate of the proposed method was 86.91%, which was 3.26%, 6.35%, 16.43%, 3.08% and 4.7% higher than those of the other five methods, respectively. The false positive rate and false negative rate were 0.88% and 10.53%, which gained an advantage over those of the other five segmentation algorithms. The localization error was 3.65%. In conclusion, the proposed method can accurately segment green fruit targets, which can lay the foundation for intelligent management of fruits over the entire growing season.

准确识别绿色水果目标是水果生长监测和产量估算的关键技术之一。为了解决由于果皮和叶片颜色相似而导致的水果识别错误的问题，提出了一种在自然环境中进行绿色苹果逐步检测的方法。进行了基于模糊集理论的图像增强，使水果目标在整个图像中更加突出。然后，通过基于注意力的信息最大化（AIM）算法粗略确定水果区域，并根据自适应像素扩展方法对识别出的苹果区域进行裁剪，以去除背景信息。之后，通过融合光照不变图像和R来完成水果目标的精确分割-裁切后图像的成分。为了评估该方法的性能，将其与基于照明不变性理论的算法，均值漂移算法，K均值聚类算法，流形排序算法和GrabCut算法进行了比较。该测试是使用200种绿色苹果图像在不同生长状态下进行的。实验结果表明，该方法的分割率为86.91％，分别比其他五种方法分别高3.26％，6.35％，16.43％，3.08％和4.7％。假阳性率和假阴性率分别为0.88％和10.53％，比其他五种分割算法有优势。定位误差为3.65％。总而言之，该方法可以准确地分割绿色水果目标，