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Updated: May 10, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

A Comprehensive Review of Deep Learning in Computer Vision for Monitoring Apple Tree Growth and Fruit Production.

Meng Lv¹, Yi-Xiao Xu¹, Yu-Hang Miao¹

¹College of Engineering, China Agricultural University, 17 Qinghua East Road, Haidian, Beijing 100083, China.

Sensors (Basel, Switzerland)

|April 26, 2025

Summary

Deep learning and computer vision enhance apple orchard management by monitoring tree growth and fruit production. This review highlights models for pest detection, organ growth analysis, and yield estimation, crucial for smart farming.

Keywords:

apple tree growth computer vision fruit production smart orchard target recognition

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Area of Science:

Agricultural Science
Computer Science
Artificial Intelligence

Background:

Apple cultivation faces challenges from suboptimal tree growth and labor-intensive orchard operations, impacting profitability.
Technological advancements are needed to optimize apple production and ensure economic viability.

Purpose of the Study:

To review the application of deep learning and computer vision techniques in monitoring apple tree growth and fruit production over the last seven years.
To identify effective deep learning models for various orchard monitoring tasks.

Main Methods:

A comprehensive literature review of deep learning and computer vision applications in apple orchards.
Analysis of three deep learning model categories: detection (YOLO, Faster R-CNN), classification (AlexNet, ResNet), and segmentation (SegNet, Mask R-CNN).

Main Results:

Deep learning models successfully applied for detecting pests and diseases on various tree parts.
Models demonstrated effectiveness in monitoring organ growth (fruits, blossoms, branches) and estimating yield.
Post-harvest fruit defect detection was also achieved using these advanced techniques.

Conclusions:

Deep learning and computer vision offer powerful tools for real-time monitoring and management in apple orchards.
The study outlines current research, discusses model advantages/disadvantages, and identifies future trends for smart apple farming.
This research is vital for the development and implementation of intelligent orchard systems.