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Journals / Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering / 2023 / Cilt: 65 Sayı: 2

Comparative analysis of mature tomato detection by feature extraction and machine learning for autonomous greenhouse robots

Hakki Alparslan ILGIN
Fevzi Anıl AYDEMİR
Berkay CEDİMOĞLU
Muhammet Nurullah AYDIN
Turkey-hasan SİLLELİ
Pages
100–114
Publication Date
2023-12-29
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Performance of the detection is improved by eliminating false positive results at the postprocessing stage using color information.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Accurate detection of tomatoes grown in greenhouses is important for timely harvesting.
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Key Findings & Data:For these reasons, high-performance automatic detection of tomatoes by greenhouse robots has become an increasingly studied area today.
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Conclusion & Impact:Performance of the detection is improved by eliminating false positive results at the postprocessing stage using color information.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Tomato detection · harvesting robots · machine learning

Abstract

Accurate detection of tomatoes grown in greenhouses is important for timely harvesting. In this way, it is ensured that mature tomatoes are collected by distinguishing them from the unripe ones. Insufficient light, occlusion, and overlapping adversely affect the detection of mature tomatoes. In addition, it is time consuming for people to detect mature tomatoes at certain periods in large greenhouses. For these reasons, high-performance automatic detection of tomatoes by greenhouse robots has become an increasingly studied area today. In this paper, two feature extraction methods, histogram of oriented gradients (HOG) and local binary patterns (LBP), which are effective in object recognition, and two important and commonly used classifiers of machine learning, support vector machines (SVM) and k-nearest neighbor (kNN), are comparatively used to detect and count tomatoes. The HOG and LBP features are classified separately and together by SVM or kNN, and the success of each case are compared. Performance of the detection is improved by eliminating false positive results at the postprocessing stage using color information.

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Cite This Article

ILGIN, H. A., AYDEMİR, F. A., CEDİMOĞLU, B., AYDIN, M. N., SİLLELİ, T. (2023). Comparative analysis of mature tomato detection by feature extraction and machine learning for autonomous greenhouse robots. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, Cilt: 65 Sayı: 2, 100–114. https://ajindex.com/articles/971356

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