Wheat Ear Recognition Based on RetinaNet and Transfer Learning
WEBJul 16, 2021 · The results show that using the Global WHEAT dataset for recognition, the RetinaNet method, and the Faster R-CNN method achieve an average accuracy of 0.82 and 0.72, with the RetinaNet method obtaining the highest recognition accuracy.
Wheat Ear Recognition Based on RetinaNet and Transfer Learning …
WEBJul 16, 2021 · This study provides technical support and a reference for automatic wheat ear recognition and yield estimation. The number of wheat ears is an essential indicator for wheat production and yield estimation, but accurately obtaining wheat ears requires expensive manual cost and labor time.
CTWheatNet: Accurate detection model of wheat ears in field
WEBTo meet the requirements of field yield estimation and improve wheat ear detection accuracy, this paper proposes the CTWheatNet model, which integrates CNN and Transformer to overcome their respective limitations in wheat ear detection tasks and achieve precise detection of wheat ears in the field.
Wheat Ears Counting in Field Conditions Based on Multi-Feature ...
WEBJul 12, 2018 · The wheat ear is an important agronomic component (Jin et al., 2017) not only is closely associated with yield but also plays an important role in disease detection, nutrition examination, and growth-period determination.
Generalized Focal Loss WheatNet (GFLWheatNet): Accurate ... - MDPI
WEBJun 6, 2024 · Generalized Focal Loss WheatNet (GFLWheatNet): Accurate Application of a Wheat Ear Detection Model in Field Yield Prediction. by. Yujie Guan. 1, Jiaqi Pan. 1, Qingqi Fan. 2, Liangliang Yang. 3, Li Xu. 4,* and. Weikuan Jia. 1,5,* School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China. 2.
Agronomy | Free Full-Text | Cross-Platform Wheat Ear Counting …
WEBJul 4, 2023 · Wheat has the largest sown area, the largest yield, and is the most widely distributed food crop in the world. The number of ears of wheat is one of the most critical parameters in wheat phenotyping research, which is the key evaluation parameter for wheat planting density and yield estimation.
Deep learning for wheat ear segmentation and ear density …
WEBAug 1, 2022 · An unsupervised learning approach on the YOLOv5 model, as well as the cutting-edge DeepMAC segmentation method were exploited to develop a wheat ear counting and segmentation pipeline that necessitated only a …
Wheat Ear Segmentation Based on a Multisensor System and …
WEBJan 28, 2022 · The objective of this paper is to propose a simple method for automatic wheat ear segmentation. The method relies on the classification of simple linear iterative clustering (SLIC) superpixels using features extracted from the fusion of RGB and multispectral images.
Wheat ear counting in-field conditions: high throughput and low …
WEBMar 17, 2018 · Developing robust, low-cost and efficient field methods to assess wheat ear density, as a major agronomic component of yield, is highly relevant for phenotyping efforts towards increases in grain yield.
A method for detecting the number of wheat ears based on the …
WEBMar 26, 2024 · This study provides a reliable method for precision wheat ear identification, offering valuable applications in high-throughput investigation of wheat ear traits. KEYWORDS: precision agriculture