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Related Experiment Videos

Accurate orange yield estimation using a novel dataset, fine-tuned deep learning models, and vision-LLM benchmarking.

Qurat Ul Ain Akram1, Zeeshan Ramzan1, Waqas Ali2

  • 1Department of Computer Science, New Campus, University of Engineering and Technology, Lahore, Pakistan.

Scientific Reports
|July 10, 2026
PubMed
Summary

Related Concept Videos

Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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This study introduces a new dataset and methods for accurate orange yield estimation, addressing challenges like occlusion and varying conditions. Faster R-CNN achieved the best results, improving precision agriculture for fruit farming.

Area of Science:

  • Computer Vision and Machine Learning in Agriculture
  • Precision Agriculture Technologies
  • Fruit Yield Estimation using Deep Learning

Background:

  • Traditional manual orange yield estimation is labor-intensive, time-consuming, and error-prone.
  • Deep learning offers potential for enhanced fruit detection and yield estimation in precision agriculture.
  • Challenges in orange yield estimation include fruit density, occlusion, lighting variations, and image quality.

Purpose of the Study:

  • To develop a comprehensive dataset for orange yield estimation, accounting for variations in size, lighting, and occlusion.
  • To benchmark state-of-the-art deep learning models for accurate orange detection and yield estimation.
  • To evaluate the performance of Vision Large Language Models (VLLMs) in zero-shot orange yield estimation.

Main Methods:

Keywords:
Deep LearningObject detectionOrange yield estimationVision large language models

Related Experiment Videos

  • Collected a dataset of 9,781 images from 41 videos, annotated with bounding boxes for single oranges and bunches (2-4 oranges).
  • Fine-tuned three multiclass object detection models (Faster R-CNN, Mask R-CNN, YOLOv8n) on the annotated dataset.
  • Performed zero-shot evaluation of Vision Large Language Models (Gemini and LLaMA) for yield estimation.

Main Results:

  • Faster R-CNN achieved the best performance with a Mean Absolute Percentage Error (MAPE) of 18.22% and an R-squared value of 0.95.
  • Gemini and LLaMA showed promising zero-shot capabilities with MAPE values of 52.05% and 103.77%, respectively.
  • The developed dataset and annotation strategy effectively handle occluded oranges and variations in image conditions.

Conclusions:

  • The created dataset is valuable for advancing research in orange detection and yield estimation.
  • Faster R-CNN demonstrates superior performance for orange yield estimation compared to Mask R-CNN and YOLOv8n.
  • Further research is needed to improve the accuracy of VLLMs for practical agricultural applications.