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

Şahin Yıldırım¹, Burak Ulu¹

¹Department of Mechatronic Engineering, Erciyes University, Kayseri 38039, Turkey.

Sensors (Basel, Switzerland)

|July 14, 2023

Summary

This study demonstrates that deep learning models, specifically Faster Region-CNN (Faster R-CNN) and Single-Shot Multibox Detection (SSD) Mobilenet, can accurately detect and count apples using a custom dataset. The Faster R-CNN model achieved high accuracy, aiding in precise yield forecasting for apple producers.

Keywords:

aerial robotics agricultural automation computer-vision deep learning object detection

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Area of Science:

Computer Vision
Machine Learning
Agricultural Technology

Background:

Convolution Neural Network (CNN) deep learning is prevalent for fruit detection and classification based on visual characteristics.
Accurate fruit yield forecasting is crucial for producers to establish commercial agreements.

Purpose of the Study:

To evaluate the performance of two deep learning models, Single-Shot Multibox Detection (SSD) Mobilenet and Faster Region-CNN (Faster R-CNN), for autonomous apple detection and counting.
To compare the effectiveness of these models when trained on a custom dataset versus pre-trained COCO datasets.
To enable accurate yield predictions for apple producers through automated orchard monitoring.

Main Methods:

Generated a custom dataset of 4000 red apple images for training.
Trained SSD-Mobilenet and Faster R-CNN models using the custom dataset with a learning rate between 0.015-0.04.
Developed a Flying Robotic System (FRS) for autonomous apple detection and counting in an orchard.
Experimentally compared the performance of the trained models.

Main Results:

Both SSD-Mobilenet and Faster R-CNN models achieved accuracy rates up to 93%.
The Faster R-CNN model demonstrated highly successful detection performance, reducing the loss value below 0.1.
Autonomous detection and counting were successfully implemented in a commercial apple orchard.

Conclusions:

Faster R-CNN and SSD-Mobilenet are effective deep learning architectures for autonomous apple detection and yield estimation.
Training with a custom dataset significantly enhances model performance for specific apple varieties.
Automated systems utilizing these models can provide valuable tools for agricultural producers to improve yield forecasting.