Non-Destructive Assessment of Microbial Spoilage of Broiler Breast Meat Using Structured Illumination Reflectance Imaging with Machine Learning DOI
Ebenezer O. Olaniyi, Yuzhen Lu, Xin Zhang

et al.

Food Analytical Methods, Journal Year: 2024, Volume and Issue: 17(5), P. 652 - 663

Published: Feb. 29, 2024

Language: Английский

Detection of rice panicle density for unmanned harvesters via RP-YOLO DOI

Jingwei Sun,

Jun Zhou,

Yongqiang He

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 226, P. 109371 - 109371

Published: Aug. 29, 2024

Language: Английский

Citations

5
Precise extraction of targeted apple tree canopy with YOLO-Fi model for advanced UAV spraying plans DOI
Wei Peng, Xiaojing Yan, Wentao Yan

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 226, P. 109425 - 109425

Published: Sept. 10, 2024

Language: Английский

Citations

5
Deep learning-enhanced remote sensing-integrated crop modeling for rice yield prediction DOI Creative Commons
Seungtaek Jeong, Jonghan Ko,

Jong-Oh Ban

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 84, P. 102886 - 102886

Published: Nov. 9, 2024

Language: Английский

Citations

5
Image patch-based deep learning approach for crop and weed recognition DOI Creative Commons
A S M Mahmudul Hasan, Dean Diepeveen, Hamid Laga

et al.

Ecological Informatics, Journal Year: 2023, Volume and Issue: 78, P. 102361 - 102361

Published: Nov. 3, 2023

Accurate classification of weed species in crop plants plays a crucial role precision agriculture by enabling targeted treatment. Recent studies show that artificial intelligence deep learning (DL) models achieve promising solutions. However, several challenging issues, such as lack adequate training data, inter-class similarity between and intra-class dissimilarity the images same at different growth stages or for other reasons (e.g., variations lighting conditions, image capturing mechanism, agricultural field environments) limit their performance. In this research, we propose an based pipeline where patch is considered time to improve We first enhance using generative adversarial networks. The enhanced are divided into overlapping patches, subset which used DL models. For selecting most informative use variance Laplacian mean frequency Fast Fourier Transforms. At test time, model's outputs fused weighted majority voting technique infer class label image. proposed was evaluated 10 state-of-the-art on four publicly available datasets: DeepWeeds, Cotton weed, Corn Tomato weed. Our achieved significant performance improvements all datasets. DenseNet201 top with F1 scores 98.49%, 99.83% 100% Deepweeds, datasets, respectively. highest score dataset 98.96%, obtained InceptionResNetV2. Moreover, addressed issues DeepWeeds more accurately classified minority classes dataset. This indicates can be farming applications.

Language: Английский

Citations

11
Non-Destructive Assessment of Microbial Spoilage of Broiler Breast Meat Using Structured Illumination Reflectance Imaging with Machine Learning DOI
Ebenezer O. Olaniyi, Yuzhen Lu, Xin Zhang

et al.

Food Analytical Methods, Journal Year: 2024, Volume and Issue: 17(5), P. 652 - 663

Published: Feb. 29, 2024

Language: Английский

Citations

4