.png){kind=icon}
返回
A Precise Plot-Level Rice Yield Prediction Method Based on Panicle Detection
.png){kind=icon}
OA
AI
DOI：10.3390/agronomy14081618.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Accurately estimating rice yield is essential for ensuring global food security, enhancing agricultural productivity, and promoting agricultural economic growth. This study constructed a dataset of rice panicles at different growth stages and combined it with an attention mechanism and the YOLOv8 network to propose the YOLOv8s+LSKA+HorNet rice panicle detection and counting model, based on a drone remote sensing platform. Using the panicle count data collected by this model, along with the thousand-grain weight, number of grains per panicle, and actual yield data from a rice nitrogen gradient experimental field, various machine learning models were trained to ultimately propose a field-level rapid rice yield estimation model, RFYOLO. The experimental results show that the rice panicle detection and counting model can achieve an average precision (AP) of 98.0% and a detection speed of 20.3 milliseconds. The final yield estimation model achieved a prediction R2 value of 0.84. The detection and counting model significantly reduced missed and duplicate detections of rice panicles. Additionally, this study not only enhanced the model's generalization ability and practicality through algorithmic innovation but also verified the impact of yield data range on the stability of the estimation model through the rice nitrogen gradient experiment. This is significant for early rice yield estimation and helping agricultural producers make more informed planting decisions.
Keyword:
deep learning
rice panicle detection
precise rice panicle counting
rice yield estimation
期刊
A
IF:
3.4
论文数:
1.7W
被引数:
5.0W
机构
暂无机构信息
引用论文
Post-Pinatubo Evolution and Subsequent Trend of the Stratospheric Aerosol Layer Observed by Mid-Latitude Lidars in Both Hemispheres.png)
SOLAOAAI
SOLAIF0
OAAI分享收藏Deep Learning Enables Instant and Versatile Estimation of Rice Yield Using Ground-Based RGB ImagesPLANT PHENOMICS
OAAI
PLANT PHENOMICSIF6.4
OAAI分享收藏Growth and life histories in Nematoda with particular reference to environmental factorsNematology
NematologyIF0
PREAI
分享收藏Identification of SMG3, a QTL Coordinately Controls Grain Size, Grain Number per Panicle, and Grain Weight in RiceSMG3的鉴定，QTL协调控制水稻的籽粒大小，每穗粒数和粒重OAAI
OAAI分享收藏Refined feature fusion for in-field high-density and multi-scale rice panicle counting in UAV imagesOAAI
OAAI分享收藏Rapid Detection and Counting of Wheat Ears in the Field Using YOLOv4 with Attention Module使用带有注意模块的YOLOv4快速检测和计数田间小麦穗AGRONOMY-BASELOAAI
AGRONOMY-BASELIF3.4
OAAI分享收藏Enhanced efficiency nitrogen fertilizers for rice systems: Meta-analysis of yield and nitrogen uptakeFIELD CROPS RESEARCH
FIELD CROPS RESEARCHIF6.4
PREAI
分享收藏
查看更多内容