Accurate Fruit Phenotype Reconstruction via Geometry-Smooth Neural Implicit Surface

Wei Ying, Kewei Hu, Ayham Ahmed, Zhenfeng Yi, Junhong Zhao and Hanwen Kang ()
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Wei Ying: College of Engineering, South China Agriculture University, Guangzhou 510642, China
Kewei Hu: Department of Mechanical and Areospace Engineering, Monash University, Clayton, VIC 3800, Australia
Ayham Ahmed: Department of Mechanical and Areospace Engineering, Monash University, Clayton, VIC 3800, Australia
Zhenfeng Yi: College of Engineering, South China Agriculture University, Guangzhou 510642, China
Junhong Zhao: Institute of Facility Agriculture, Guangdong Academic of Agriculture Science, Guangzhou, 510640, China
Hanwen Kang: Department of Mechanical and Areospace Engineering, Monash University, Clayton, VIC 3800, Australia

Agriculture, 2024, vol. 14, issue 12, 1-18

Abstract: Accurate collection of plant phenotyping is critical to optimising sustainable farming practices in precision agriculture. Traditional phenotyping in controlled laboratory environments, while valuable, falls short in understanding plant growth under real-world conditions. Emerging sensor and digital technologies offer a promising approach for direct phenotyping of plants in farm environments. This study investigates a learning-based phenotyping method using neural implicit surfaces reconstruction to achieve accurate in situ phenotyping of pepper plants in greenhouse environments. To quantitatively evaluate the performance of this method, traditional point cloud registration on 3D scanning data is implemented for comparison. Experimental result shows that NIR (neural implicit surfaces reconstruction) achieves competitive accuracy compared to the 3D scanning method. The mean distance error between the scanner-based method and the NeRF (neural radiance fields)-based method is 0.811 mm. This study shows that the learning-based NeRF method has similar accuracy to the 3D scanning-based method but with greater scalability and faster deployment capabilities.

Keywords: deep learning; robotics; NeRF; phenotyping (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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