Deep Learning Based Apples Counting for Yield Forecast Using Proposed Flying Robotic System.

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.

Plant phenotyping relevance

リンゴ果実の画像検出・計数による収量推定を、飛行ロボットと深層学習モデルで開発・比較・評価しており、植物器官の形質取得が中心的です。

Code and data availability

The paper's custom apple image dataset (4000 labeled images), trained SSD-Mobilenet/Faster R-CNN models, and analysis code are not publicly deposited. The Data Availability Statement says the data are available only on request from the corresponding author. The TensorFlow 2 Detection Model Zoo URL is a generic third‑ly

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