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Related Experiment Video

Updated: Oct 17, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

Yield Estimation and Visualization Solution for Precision Agriculture.

Youssef Osman¹, Reed Dennis¹, Khalid Elgazzar¹

¹Faculty of Engineering and Applied Science, Ontario Tech University, Oshawa, ON L1H 7K4, Canada.

Sensors (Basel, Switzerland)

|October 13, 2021

Summary

This study introduces a smart harvesting solution using object detection and tracking for precision agriculture. The system accurately estimates crop yield and optimizes container placement, improving farm logistics.

Keywords:

computer vision data analysis deep learning geospatial mapping precision agriculture

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Area of Science:

Agricultural Engineering
Computer Vision
Robotics

Background:

Precision agriculture demands efficient yield estimation and logistical planning.
Existing object tracking models often lack generalizability for diverse crop types.

Purpose of the Study:

To develop an end-to-end smart harvesting solution for precision agriculture.
To enhance object tracking for accurate fruit counting in videos.
To create a decision support system for optimizing harvest logistics.

Main Methods:

Utilized You Only Look Once (YOLO) for object detection and ResNet-integrated DeepSORT for fruit tracking.
Trained models on video data of apples, oranges, and pumpkins.
Incorporated geospatial data for yield visualization and optimal container placement.

Main Results:

Achieved 91-95% accuracy in yield estimation for apples.
Demonstrated 79-93.9% accuracy for oranges and pumpkins without retraining.
Developed a system for visualizing yield distribution and optimizing container placement.

Conclusions:

The proposed ResNet-based DeepSORT enhances fruit counting accuracy and generalizability.
The integrated geospatial visualization and container placement solution supports efficient farm logistics.
This framework provides a blueprint for advanced agricultural decision support systems.