Geometric Feature Characterization of Apple Trees from 3D LiDAR Point Cloud Data DOI Creative Commons
Md Rejaul Karim, Shahriar Ahmed, Md Nasim Reza

et al.

Journal of Imaging, Journal Year: 2024, Volume and Issue: 11(1), P. 5 - 5

Published: Dec. 31, 2024

The geometric feature characterization of fruit trees plays a role in effective management orchards. LiDAR (light detection and ranging) technology for object enables the rapid precise evaluation features. This study aimed to quantify height, canopy volume, tree spacing, row spacing an apple orchard using three-dimensional (3D) sensor. A sensor was used collect 3D point cloud data from orchard. Six samples trees, representing variety shapes sizes, were selected collection validation. Commercial software python programming language utilized process collected data. processing steps involved conversion, radius outlier removal, voxel grid downsampling, denoising through filtering erroneous points, segmentation region interest (ROI), clustering density-based spatial (DBSCAN) algorithm, transformation, removal ground points. Accuracy assessed by comparing estimated outputs with corresponding measured values. sensor-estimated heights 3.05 ± 0.34 m 3.13 0.33 m, respectively, mean absolute error (MAE) 0.08 root squared (RMSE) 0.09 linear coefficient determination (r2) 0.98, confidence interval (CI) −0.14 −0.02 high concordance correlation (CCC) 0.96, indicating strong agreement accuracy. volumes 13.76 2.46 m3 14.09 2.10 m3, MAE 0.57 RMSE 0.61 r2 value 0.97, CI −0.92 0.26, demonstrating precision. For distances 3.04 0.17 3.18 0.24 3.35 3.40 0.05 values 0.12 0.92 0.07 0.94 respectively. −0.18 0.01, −0.1, 0.002 Although minor differences observed, estimates efficient, though specific measurements require further refinement. results are based on limited dataset six values, providing initial insights into performance. However, larger would offer more reliable accuracy assessment. small sample size (six trees) limits generalizability findings necessitates caution interpreting results. Future studies should incorporate broader diverse validate refine characterization, enhancing practices

Language: Английский

Visual Navigation and Crop Mapping of a Phenotyping Robot MARS-PhenoBot in Simulation DOI Creative Commons
Zhengkun Li, Rui Xu, Changying Li

et al.

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100910 - 100910

Published: March 1, 2025

Language: Английский

Citations

0
Image Recognition Technology in Smart Agriculture: A Review of Current Applications Challenges and Future Prospects DOI Open Access

Chunxia Jiang,

Kangshu Miao,

Zhichao Hu

et al.

Processes, Journal Year: 2025, Volume and Issue: 13(5), P. 1402 - 1402

Published: May 4, 2025

The implementation of image recognition technology can significantly enhance the levels automation and intelligence in smart agriculture. However, most researchers focused on its applications medical imaging, industry, transportation, while fewer Based this, this study aims to contribute comprehensive understanding application agriculture by investigating scientific literature related last few years. We discussed analyzed plant disease pest detection, crop species identification, yield prediction, quality assessment. Then, we made a brief introduction soil testing nutrient management, as well agricultural machinery operation assessment product grading. At last, challenges emerging trends were summarized. results indicated that models used face such limited generalization, real-time processing, insufficient dataset diversity. Transfer learning green Artificial Intelligence (AI) offer promising solutions these issues reducing reliance large datasets minimizing computational resource consumption. Advanced technologies like transformers further adaptability accuracy This review provides valuable information current state prospective future opportunities.

Language: Английский

Citations

0
Geometric Feature Characterization of Apple Trees from 3D LiDAR Point Cloud Data DOI Creative Commons
Md Rejaul Karim, Shahriar Ahmed, Md Nasim Reza

et al.

Journal of Imaging, Journal Year: 2024, Volume and Issue: 11(1), P. 5 - 5

Published: Dec. 31, 2024

The geometric feature characterization of fruit trees plays a role in effective management orchards. LiDAR (light detection and ranging) technology for object enables the rapid precise evaluation features. This study aimed to quantify height, canopy volume, tree spacing, row spacing an apple orchard using three-dimensional (3D) sensor. A sensor was used collect 3D point cloud data from orchard. Six samples trees, representing variety shapes sizes, were selected collection validation. Commercial software python programming language utilized process collected data. processing steps involved conversion, radius outlier removal, voxel grid downsampling, denoising through filtering erroneous points, segmentation region interest (ROI), clustering density-based spatial (DBSCAN) algorithm, transformation, removal ground points. Accuracy assessed by comparing estimated outputs with corresponding measured values. sensor-estimated heights 3.05 ± 0.34 m 3.13 0.33 m, respectively, mean absolute error (MAE) 0.08 root squared (RMSE) 0.09 linear coefficient determination (r2) 0.98, confidence interval (CI) −0.14 −0.02 high concordance correlation (CCC) 0.96, indicating strong agreement accuracy. volumes 13.76 2.46 m3 14.09 2.10 m3, MAE 0.57 RMSE 0.61 r2 value 0.97, CI −0.92 0.26, demonstrating precision. For distances 3.04 0.17 3.18 0.24 3.35 3.40 0.05 values 0.12 0.92 0.07 0.94 respectively. −0.18 0.01, −0.1, 0.002 Although minor differences observed, estimates efficient, though specific measurements require further refinement. results are based on limited dataset six values, providing initial insights into performance. However, larger would offer more reliable accuracy assessment. small sample size (six trees) limits generalizability findings necessitates caution interpreting results. Future studies should incorporate broader diverse validate refine characterization, enhancing practices

Language: Английский

Citations

0