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Abstract：
Nowadays, Convolution Neural Network (CNN) based deep learning methods are widely used in detecting and classifying fruits from faults, color and size characteristics. In this study, two different neural network model estimators are employed to detect apples using the Single-Shot Multibox Detection (SSD) Mobilenet and Faster Region-CNN (Faster R-CNN) model architectures, with the custom dataset generated from the red apple species. Each neural network model is trained with created dataset using 4000 apple images. With the trained model, apples are detected and counted autonomously using the developed Flying Robotic System (FRS) in a commercially produced apple orchard. In this way, it is aimed that producers make accurate yield forecasts before commercial agreements. In this paper, SSD-Mobilenet and Faster R-CNN architecture models trained with COCO datasets referenced in many studies, and SSD-Mobilenet and Faster R-CNN models trained with a learning rate ranging from 0.015-0.04 using the custom dataset are compared experimentally in terms of performance. In the experiments implemented, it is observed that the accuracy rates of the proposed models increased to the level of 93%. Consequently, it has been observed that the Faster R-CNN model, which is developed, makes extremely successful determinations by lowering the loss value below 0.1.
摘要：
如今,基于卷积神经网络(CNN)的深度学习方法被广泛应用于从故障中检测和分类水果,颜色和尺寸特征。在这项研究中,采用两种不同的神经网络模型估计器，使用单点多盒检测（SSD）Mobilenet和FasterRegion-CNN（FasterR-CNN）模型架构来检测苹果，使用从红苹果物种生成的自定义数据集。每个神经网络模型都使用4000个苹果图像使用创建的数据集进行训练。使用经过训练的模型，在商业生产的苹果园中使用开发的飞行机器人系统（FRS）自主检测和计数苹果。这样,旨在使生产者在达成商业协议之前做出准确的产量预测。在本文中,使用许多研究中引用的COCO数据集训练的SSD-Mobilenet和FasterR-CNN架构模型，和SSD-Mobilenet和使用自定义数据集训练的学习率范围为0.015-0.04的FasterR-CNN模型在性能方面进行了实验比较。在实施的实验中，据观察，所提出的模型的准确率提高到93%的水平。因此,已经观察到，更快的R-CNN模型，这是开发的，通过将损失值降低到0.1以下，可以做出非常成功的确定。
