DOI: 10.20965/jaciii.2024.p0352 ISSN: 1883-8014

Estimating Tomato Plant Leaf Area Using Multiple Images from Different Viewing Angles

Nobuhiko Yamaguchi, Hiroshi Okumura, Osamu Fukuda, Wen Liang Yeoh, Munehiro Tanaka
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

The estimation of leaf area is an important measure for understanding the growth, development, and productivity of tomato plants. In this study, we focused on the leaf area of a potted tomato plant and proposed methods, namely, NP, D2, and D3, for estimating its leaf area. In the NP method, we used multiple tomato plant images from different viewing angles to reduce the estimation error of the leaf area, whereas in the D2 and D3 methods, we further compensated for the perspective effects. The performances of the proposed methods were experimentally assessed using 40 “Momotaro Peace” tomato plants. The experimental results confirmed that the NP method had a smaller mean absolute percentage error (MAPE) on the test set than the conventional estimation method that uses a single tomato plant image. Likewise, the D2 and D3 methods had a smaller MAPE on the test set than the conventional method that did not compensate for perspective effects.

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