结合二维图像和点云深度学习预测小麦地上生物量

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Shaolong Zhu, Weijun Zhang, Tianle Yang, Fei Wu, Yihan Jiang, Guanshuo Yang, Muhammad Zain, Yuanyuan Zhao, Zhaosheng Yao, Tao Liu, Chengming Sun
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引用次数: 0

摘要

目的 使用无人飞行器(UAV)数据预测作物地上生物量(AGB)正在成为破坏性方法的一种更可行的替代方法。然而,冠层高度、植被指数(VI)和其他传统特征在作物生长的中后期会趋于饱和,从而严重影响 AGB 预测的准确性。结果结果表明,融合点云深度特征后,VI、CI、TI 和冠层高度模型图像预测的 R2 值分别增加了 0.05、0.08、0.06 和 0.07。对于 VI、CI 和 TI 的组合,R2 从 0.86 增加到最大 0.9,而均方根误差(RMSE)和平均绝对误差分别为 1.80 吨/公顷和 1.36 吨/公顷。此外,我们的研究结果表明,混合融合的准确度最高,它在预测不同年份、不同生长阶段、不同作物品种、不同氮肥施用量和不同密度的 AGB 方面表现出了强大的适应性。 结论 本研究有效地解决了光谱和化学信息饱和的问题,为高精度表型和先进的作物田间管理提供了有价值的见解,并为研究其他作物和表型参数提供了参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Combining 2D image and point cloud deep learning to predict wheat above ground biomass

Combining 2D image and point cloud deep learning to predict wheat above ground biomass

Purpose

The use of Unmanned aerial vehicle (UAV) data for predicting crop above-ground biomass (AGB) is becoming a more feasible alternative to destructive methods. However, canopy height, vegetation index (VI), and other traditional features can become saturated during the mid to late stages of crop growth, significantly impacting the accuracy of AGB prediction.

Methods

In 2022 and 2023, UAV multispectral, RGB, and light detection and ranging point cloud data of wheat populations were collected at seven growth stages across two experimental fields. The point cloud depth features were extracted using the improved PointNet++ network, and AGB was predicted by fusion with VI, color index (CI), and texture index (TI) raster image features.

Results

The findings indicate that when the point cloud depth features were fused, the R2 values predicted from VI, CI, TI, and canopy height model images increased by 0.05, 0.08, 0.06, and 0.07, respectively. For the combination of VI, CI, and TI, R2 increased from 0.86 to a maximum of 0.9, while the root-mean-square error (RMSE) and mean absolute error were 1.80 t ha−1 and 1.36 t ha−1, respectively. Additionally, our findings revealed that the hybrid fusion exhibits the highest accuracy, it demonstrates robust adaptability in predicting AGB across various years, growth stages, crop varieties, nitrogen fertilizer applications, and densities.

Conclusion

This study effectively addresses the saturation in spectral and chemical information, provides valuable insights for high-precision phenotyping and advanced crop field management, and serves as a reference for studying other crops and phenotypic parameters.

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来源期刊
Precision Agriculture
Precision Agriculture 农林科学-农业综合
CiteScore
12.30
自引率
8.10%
发文量
103
审稿时长
>24 weeks
期刊介绍: Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.
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