Abstract: It is an important way to solve the shortage of rural labor force and reduce the cost of fruit picking to realize mechanized intelligent picking in orchards. Accurate identification of fruit in orchards is the key technology. We took jujube as the research object. In order to establish a maturity identification model suitable for multiple varieties and strong practicability, the jujube fruits of many varieties in natural environment were divided into mature fruit, immature fruit and ripe fruit, semired fruit and immature fruit labeling methods, and four recognition models based on YOLO V3, YOLO V4, YOLO V4-Tiny and Mobilenet-YOLO V4-Lite were established. The study showed that both YOLO V3 and YOLO V4-Tiny models in the YOLO algorithm could be applied to the two labeling methods, and the verification set mAP was about 94%, which proved that the YOLO algorithm could effectively identify the maturity of jujube fruits.

Key words: jujube, maturity, YOLO algorithm, target detection, intelligence gathering

摘要： 实现果园机械化智能采摘是解决农村劳动力不足、降低果实采摘成本的重要途径，对果园中果实的准确识别是其关键技术。以枣为研究对象，建立最适合多品种、实用性强的枣果实成熟度识别模型，将YOLO算法引入到枣果实在自然环境下的成熟度识别中，将枣果实分为成熟果实、未熟果实和完熟果实、半红果实、未熟果实两种标注方式，建立YOLO V3、YOLO V4、YOLO V4-Tiny和Mobilenet-YOLO V4-Lite四种识别模型。研究表明YOLO算法中YOLO V3与YOLO V4-Tiny两个模型均可适用于两种标注方式，验证集mAP约为94%，证明YOLO算法能够对枣果实进行有效的成熟度识别。

关键词: 枣果实, 成熟度, YOLO算法, 目标检测, 智能采摘