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Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV
European Journal of Agronomy ( IF 5.2 ) Pub Date : 2020-04-01 , DOI: 10.1016/j.eja.2020.126030
O.E. Apolo-Apolo , J. Martínez-Guanter , G. Egea , P. Raja , M. Pérez-Ruiz

Abstract Accurate and early estimation of citrus yields is important for both producers and agricultural cooperatives to be competitive and make informed decisions when selling their products. Yield estimation is key for predicting stock volumes, avoiding stock ruptures and planning harvesting operations. Visual yield estimations have traditionally been employed, resulting in inaccurate and misleading information. The main goal of this study was to develop an automated image processing methodology to detect, count and estimate the size of citrus fruits on individual trees using deep learning techniques. During 3 consecutive annual campaigns, a total of 20 trees from a commercial citrus grove were monitored using images captured from an unmanned aerial vehicle (UAV). These trees were harvested manually, and fruit sizes were measured. A Faster R-CNN Deep Learning model was trained using a custom dataset to detect oranges in the obtained images. An average standard error (SE) of 6.59 % was obtained between visual counting and the model’s fruit detection. Using the detected fruits, fruit size estimation was also performed. The promising results obtained indicate that this size estimation method can be employed for size discrimination prior to harvest. A model based on Long Short-term Memory (LSTM) was trained for yield estimation per tree and for a total yield estimation. The actual and estimated yields per tree were compared, resulting in an approximate error of SE = 4.53 % and a standard deviation of SD = 0.97 Kg. The actual total yield, the estimated total yield and the total yield estimated by an expert technician were compared. The error in the estimation by the technician was SE = 13.74 %, while the errors in the model were SE = 7.22 % and SD = 4083.58 Kg. These promising results demonstrate the potential of the present technique to provide yield estimates for citrus fruits or even other types of fruit.

中文翻译:

使用无人机估计柑橘类水果产量和大小的深度学习技术

摘要 柑橘产量的准确和早期估算对于生产者和农业合作社在销售其产品时保持竞争力和做出明智的决定都很重要。产量估算是预测库存量、避免库存破裂和规划收获作业的关键。传统上采用视觉产量估计,导致不准确和误导性信息。本研究的主要目标是开发一种自动化图像处理方法,使用深度学习技术检测、计数和估计单个树木上柑橘类水果的大小。在连续 3 年的年度活动中,使用无人机 (UAV) 捕获的图像对商业柑橘园中的 20 棵树进行了监测。这些树是人工收获的,并测量了果实的大小。Faster R-CNN 深度学习模型使用自定义数据集进行训练,以检测获得的图像中的橙子。在目视计数和模型的水果检测之间获得了 6.59% 的平均标准误差 (SE)。使用检测到的水果,还进行了水果大小估计。获得的有希望的结果表明,这种尺寸估计方法可用于收获前的尺寸区分。训练基于长短期记忆 (LSTM) 的模型用于每棵树的产量估计和总产量估计。比较了每棵树的实际产量和估计产量,得出的近似误差为 SE = 4.53 %,标准偏差为 SD = 0.97 Kg。比较实际总产量、估计总产量和专家技术人员估计的总产量。技术人员估计的误差为 SE = 13.74 %,而模型中的误差为 SE = 7.22 % 和 SD = 4083.58 Kg。这些有希望的结果证明了本技术为柑橘类水果甚至其他类型的水果提供产量估计的潜力。
更新日期:2020-04-01
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