Apple detection using filters under varying lighting conditions
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Authors
Sonala, Kiran
Author ORCID Profiles (clickable)
Degree
Master of Technologies (Computing)
Grantor
Unitec, Te Pūkenga – New Zealand Institute of Skills and Technology
Date
2025
Supervisors
Song, Lei
Sen, Sachin
Sen, Sachin
Type
Masters Thesis
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
apples
harvesting
pattern recognition systems in agriculture
computer vision in agriculture
unsupervised image classification
image processing
vector agents
lighting
harvesting
pattern recognition systems in agriculture
computer vision in agriculture
unsupervised image classification
image processing
vector agents
lighting
ANZSRC Field of Research Code (2020)
Citation
Sonala, K. (2025). Apple detection using filters under varying lighting conditions (Unpublished document submitted in partial fulfilment of the requirements for the degree of Master of Applied Technologies (Computing)). Unitec, Te Pūkenga - New Zealand Institute of Skills and Technology
https://hdl.handle.net/10652/6846
Abstract
RESEARCH QUESTIONS
• How do different lighting conditions affect apple detection in orchard images?
• How can the lighting conditions or noise of the captured image be identified?
• What filters need to be applied to the image to get the enhanced image for object detection?
• Which image filters are most effective in enhancing apple detection under varying lighting conditions or noise levels in the image by using YOLOv9?
ABSTRACT
The advancement of intelligent farming and agricultural automation has led to the development of robotic methods for efficient apple harvesting in recent years. However, because of complicated backgrounds and fluctuating illumination, it is still challenging to identify apples in genuine orchard settings. Issues like shifting lighting intensities, shadows, and image noise complicate detection, impacting the reliability of these automated systems in real-world applications. Identifying apples under different lighting conditions, such as sunlight, shadow, darkness, and blur, presents a significant obstacle in automating apple harvesting. Traditional object detection algorithms struggle with these unpredictable lighting variations, leading to decreased accuracy. There is a need for a system that can dynamically adapt to these environmental challenges and improve apple detection reliability in varying orchard conditions.
This research investigates the integration of Support Vector Machine classification and image preprocessing techniques as a preprocessing step for YOLOv9-based object detection. By classifying images based on their lighting conditions and applying appropriate filters, the research aims to enhance image clarity before detection. The study further explores various filters, such as contrast adjustment, gamma correction, and histogram equalization, to determine their effectiveness in improving detection under specific lighting scenarios. The proposed solution involves a multi-phase approach where an SVM classifier identifies the lighting condition of each image, and the corresponding filter is applied to optimize image quality. YOLOv9, an efficient object detection model, is then used to detect apples in the pre processed images. This adaptive system enables real-time preprocessing tailored to lighting variations, ensuring that each image is processed optimally for improved feature extraction and detection accuracy.
The experimental results demonstrate that the integrated SVM-Filter-YOLOv9 pipeline significantly enhances detection rates, particularly in low-light and high-contrast environments. By employing adaptive gamma correction, contrast stretching, and colour enhancement filters, the system achieved an overall improvement of approximately 23% in apple detection accuracy compared to non-filtered images. Notably, individual filter performance was especially effective for dark conditions, showing a 47% improvement, and for sunlight conditions, with a 37% increase. This study provides a scalable and reliable framework for automated fruit detection systems, positioning it as a valuable tool for future robotic harvesting technologies.
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