Aims

In the case of uneven vegetation coverage, it is still facing many problems that using UAV-remote sensing to assess the canopy nutrient status. The research objective is to determine the optimal ground feature classification and establish the leaf nitrogen concentration (LNC) inversion model.

Methods

UAV‑Phantom 4 multispectral platform was used to acquire apple orchard images. The ground features of remote sensing image were classified with the minimum distance, maximum likelihood, and object-oriented feature extraction classifications (MDC, MLC, OFEC), while spectral vegetation indices were used to perform LNC inversion using the backpropagation neural network (BP) and extreme learning machine (ELM). Further, genetic algorithm (GA) and particle swarm optimization (PSO) were used to optimize two inversion models.

Results

Compared with the MDC, the overall accuracy of MLC increased 20.30% and 26.69% in 2021 and 2022, while the OFEC increased 30.48% and 31.04%, respectively. The LNC inversion model of BP and ELM produced an acceptable performance (R²_c > R²_p > 0.60, RRMSE < 15%). The use of GA and PSO algorithms did not improve the prediction performance of the BP inversion model. Compared with ELM, the RRMSE of GA_ELM and PSO_ELM inversion models decreased 2.63% and 20.39%. Furthermore, the PSO_ELM inversion model decreased the RRMSE 10.95% compared to the BP model.

Conclusion

The combination of reverse thinking iterative approach with the PSO algorithm significantly enhances the predictive performance of the ELM inversion model, which can present a rapid assessment method for leaf nitrogen nutrition diagnosis based on the relationship between LNC and fruit yield.

中文翻译：

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基于无人机多光谱影像的苹果园地物最优分类与叶片氮素状态评价

 目标

在植被覆盖不均的情况下，利用无人机遥感评估冠层养分状况仍面临诸多问题。研究目标是确定最优地貌分类并建立叶片氮浓度 （LNC） 反演模型。

 方法

UAV-Phantom 4 多光谱平台用于采集苹果园图像。遥感影像的地面特征采用最小距离、最大似然和面向对象特征提取分类（MDC、MLC、OFEC）进行分类，而光谱植被指数则采用反向传播神经网络（BP）和极限学习机（ELM）进行LNC反演。此外，采用遗传算法 （GA） 和粒子群优化 （PSO） 优化两个反演模型。

 结果

与 MDC 相比，MLC 的整体准确率在 2021 年和 2022 年分别提高了 20.30% 和 26.69%，而 OFEC 分别提高了 30.48% 和 31.04%。BP 和 ELM 的 LNC 反转模型产生了可接受的性能 （R²_c > R²_p > 0.60， RRMSE < 15%）。GA 和 PSO 算法的使用并没有提高 BP 反演模型的预测性能。与 ELM 相比，GA_ELM 和 PSO_ELM 反演模型的 RRMSE 分别下降了 2.63% 和 20.39%。此外，与 BP 模型相比，PSO_ELM反转模型使 RRMSE 降低了 10.95%。

 结论

逆向思维迭代方法与 PSO 算法的结合显著提高了 ELM 反演模型的预测性能，可以提出一种基于 LNC 与果实产量之间关系的叶片氮营养诊断快速评估方法。