Li Denghui, Sun Xiaoxuan, Jia Yuhang, Yao Zhongwei, Lin Peiyi, Chen Yingyi, Zhou Haobo, Zhou Zhengqi, Wu Kaixuan, Shi Linlin, Li Jun
College of Engineering, South China Agricultural University, Guangzhou, China.
Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China.
Front Plant Sci. 2023 Mar 6;14:1132909. doi: 10.3389/fpls.2023.1132909. eCollection 2023.
Longan yield estimation is an important practice before longan harvests. Statistical longan yield data can provide an important reference for market pricing and improving harvest efficiency and can directly determine the economic benefits of longan orchards. At present, the statistical work concerning longan yields requires high labor costs. Aiming at the task of longan yield estimation, combined with deep learning and regression analysis technology, this study proposed a method to calculate longan yield in complex natural environment. First, a UAV was used to collect video images of a longan canopy at the mature stage. Second, the CF-YD model and SF-YD model were constructed to identify Cluster_Fruits and Single_Fruits, respectively, realizing the task of automatically identifying the number of targets directly from images. Finally, according to the sample data collected from real orchards, a regression analysis was carried out on the target quantity detected by the model and the real target quantity, and estimation models were constructed for determining the Cluster_Fruits on a single longan tree and the Single_Fruits on a single Cluster_Fruit. Then, an error analysis was conducted on the data obtained from the manual counting process and the estimation model, and the average error rate regarding the number of Cluster_Fruits was 2.66%, while the average error rate regarding the number of Single_Fruits was 2.99%. The results show that the method proposed in this paper is effective at estimating longan yields and can provide guidance for improving the efficiency of longan fruit harvests.
龙眼产量估算是龙眼收获前的一项重要工作。统计龙眼产量数据可为市场定价和提高收获效率提供重要参考，并能直接决定龙眼果园的经济效益。目前，有关龙眼产量的统计工作需要高昂的人力成本。针对龙眼产量估算任务，结合深度学习和回归分析技术，本研究提出了一种在复杂自然环境下计算龙眼产量的方法。首先，使用无人机收集成熟期龙眼树冠的视频图像。其次，构建CF-YD模型和SF-YD模型分别识别簇生果和单果，实现直接从图像中自动识别目标数量的任务。最后，根据从实际果园采集的样本数据，对模型检测到的目标数量与实际目标数量进行回归分析，构建用于确定单棵龙眼树上簇生果数量和单个簇生果上单果数量的估算模型。然后，对人工计数过程和估算模型得到的数据进行误差分析，簇生果数量的平均误差率为2.66%，单果数量的平均误差率为2.99%。结果表明，本文提出的方法在估算龙眼产量方面是有效的，可为提高龙眼果实收获效率提供指导。
