摘要
针对树上苹果产量的早期估测问题,提出了一种利用果树图像树冠树叶与果实的信息,通过BP(Back propagation)神经网络建立模型进行苹果估产的方法。首先在苹果园内分别获取果树在苹果半熟期、成熟期的数字图像,并在苹果收获时将每棵树上的苹果称量,得到实际产量;采用图像处理方法识别出树冠上的果实及树叶;提取果实区域及树叶区域与产量相关的信息为输入,以果树实际产量为输出,建立基于BP神经网络的半熟期与成熟期估产模型,拟合度R分别达到0.928 7、0.980 4。将模型用于待估产样本,得到半熟期样本估测产量与实际产量拟合度R为0.876 6,成熟期样本估测产量与实际产量拟合度R为0.960 6。结果表明该模型具有较好的预测精度与鲁棒性。
In order to estimate apple yield in orchard automatically, a yield estimation method was presented which combined image processing and back-propagation neural network （BPNN） based on the information of leaves and apples in the tree. Firstly, digital images of apple trees were acquired, including half-ripe apples （the apple just turned red） and ripe apples （the apple totally turned red）. The actual yield of each tree was weighted in harvest time. Secondly, the fruits and leaves on the image of apple tree were identified. Some useful parameters were extracted from data which were used as input variables, and the actual yield was set as output variable. Finally, BPNN estimation yield model was built and the fitting degrees of this model were 0. 928 7 and 0. 980 4 for the half-ripe apples and ripe apples, respectively. When this model was applied on samples for yield estimation, the correlation coefficient between model and actual was 0. 876 6 in the half-ripe ones and 0. 960 6 in the ripe ones. The results indicated that both the two models had good reliability and generalization performance. It concluded that the method presented has substantial potential for apple yield estimation.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2015年第1期14-19,共6页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金资助项目(31371537)
河北农业大学理工基金资助项目(LG20140601)
关键词
苹果
估产
数字图像
神经网络
Apple Yield estimation Image Neural network
