.png){kind=icon}
返回
Developing a machine learning based cotton yield estimation framework using multi-temporal UAS data
.png){kind=icon}
PRE
AI
DOI：10.1016/j.isprsjprs.2020.09.015.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this research a machine learning framework was developed for cotton yield estimation using multi-temporal remote sensing data collected from unmanned aircraft system (UAS). The proposed machine learning model was based on an artificial neural network (ANN) and used three types of crop features derived from UAS data to predict the yield, namely; multi-temporal features including canopy cover, canopy height, canopy volume, normalized difference vegetation index (NDVI), excessive greenness index (ExG); non-temporal features including cotton boll count, boll size and boll volume, and irrigation status as a qualitative feature. The model provided low residual values with predicted yield values close to the observed yield values (R-2 similar to 0.9). ANN model performance was compared with support vector regression (SVR) and random forest regression (RFR). Comparison results revealed that ANN model outperforms SVR and RFR. Additionally, redundant features were removed using correlation analysis, and an optimal subset of features was obtained that included canopy volume, ExG, boll count, boll volume and irrigation status. Moreover, the relative significance of each feature in the optimal input feature subset was determined using sensitivity analysis. It was found that canopy volume and ExG contributed around 50% towards the corresponding yield. Finally, further analysis was performed to investigate how early in the growing season the model can accurately predict yield. It was observed that even at 70 days after planting the model predicted yield with reasonable accuracy (R-2 of 0.72 over test set). This study revealed that UAS derived multi-temporal data along with non-temporal and qualitative data can be combined within a machine learning framework to provide a reliable estimation of crop yield and provide effective understanding for crop management.
Keyword:
Precision agriculture
Cotton genotype selection
UAS
ANN
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
12.2
论文数:
4.4K
被引数:
3.2W
机构
引用论文
Investigation on the genotoxicity of different sizes of gold nanoparticles administered to the lungs of rats不同粒径纳米金对大鼠肺的遗传毒性研究
PREAI
分享收藏Controlled growth of spinel NiCo2O4 nanostructures on carbon cloth as a superior electrode for supercapacitors.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏Estimation of canopy attributes in beech forests using true colour digital images from a small fixed-wing UAV使用小型固定翼无人机的真彩色数字图像估算山毛榉林冠属性
OAAI
OAAI分享收藏Effect of Intermittent Exposure to Sunlight on Melanoma Risk among Indoor Workers and Sun-Sensitive Individuals间歇性暴露于阳光下对室内工作人员和对阳光敏感的人的黑色素瘤风险的影响OAAI
OAAI分享收藏Soybean yield prediction from UAV using multimodal data fusion and deep learning基于多模态数据融合和深度学习的无人机大豆产量预测OAAI
OAAI分享收藏
查看更多内容