Deep learning techniques and computer vision systems offer effective fruit counting solutions for farm yield estimation. However, the performance of these solutions drops when identifying different cultivars of the same fruit species. This study clarified the differences between mango fruit detection and mango cultivar identification. An original double-threshold-based classification method for fruit cultivar identification, with estimation of the misidentification rate was proposed in order to significantly increase the performance of a specialised mango fruit detection method known as Faster R-CNN. This method was applied on images of mango trees of three cultivars taken in Senegalese orchards of different existing cropping systems, with varying tree features, planting patterns and acquisition contexts. Analysis of the results focused on the contributions of fruit detection errors and fruit cultivar confusion to the overall error of the network for fruit counts by cultivar class. The shift from fruit detection to cultivar identification resulted in a drop in the average prediction rate from 92 to 68%. With its explicitly independent fruit detection and cultivar identification steps, the double-threshold-based classification method increased the prediction rate to 86%, with a maximum identification error of 0.05%. This setting also led to relative equality between the recall and the precision of each cultivar class, making the network well suited for fruit counting by cultivar class. This work opened new perspectives for decision support tools for fruit growers that could provide more appropriate yield estimates per cultivar.

深度学习技术和计算机视觉系统为农场产量估算提供了有效的水果计数解决方案。但是，在识别同一水果品种的不同品种时，这些解决方案的性能会下降。本研究阐明了芒果果实检测与芒果品种鉴定的区别。为了显着提高称为 Faster R-CNN 的专业芒果果实检测方法的性能，提出了一种用于水果品种识别的原始双阈值分类方法，并估计了错误识别率。该方法应用于在不同现有种植系统的塞内加尔果园拍摄的三个品种的芒果树图像，具有不同的树木特征、种植模式和采集环境。结果分析的重点是水果检测误差和水果品种混淆对品种类水果计数网络整体误差的影响。从水果检测到品种鉴定的转变导致平均预测率从 92% 下降到 68%。凭借其明确独立的水果检测和品种识别步骤，基于双阈值的分类方法将预测率提高到 86%，最大识别误差为 0.05%。这种设置还导致每个品种类别的召回率和精度之间的相对平等，使网络非常适合按品种类别进行水果计数。这项工作为水果种植者的决策支持工具开辟了新的视角，可以为每个品种提供更合适的产量估计。