Agronomy | Feature Papers in the “Precision and Digital Agriculture” Section
1. “Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review”
by Hao-Ran Qu and Wen-Hao Su
Agronomy 2024, 14(2), 363; https://doi.org/10.3390/agronomy14020363
Full text available online: https://www.mdpi.com/2073-4395/14/2/363
2. “Application of UAV Multispectral Imaging to Monitor Soybean Growth with Yield Prediction through Machine Learning”
by Sadia Alam Shammi, Yanbo Huang, Gary Feng, Haile Tewolde, Xin Zhang, Johnie Jenkins and Mark Shankle
Agronomy 2024, 14(4), 672; https://doi.org/10.3390/agronomy14040672
Full text available online: https://www.mdpi.com/2073-4395/14/4/672
3. “Comparative Performance of Aerial RGB vs. Ground Hyperspectral Indices for Evaluating Water and Nitrogen Status in Sweet Maize”
by Milica Colovic, Anna Maria Stellacci, Nada Mzid, Martina Di Venosa, Mladen Todorovic, Vito Cantore and Rossella Albrizio
Agronomy 2024, 14(3), 562; https://doi.org/10.3390/agronomy14030562
Full text available online: https://www.mdpi.com/2073-4395/14/3/562
4. “Fostering Agricultural Transformation through AI: An Open-Source AI Architecture Exploiting the MLOps Paradigm”
by Antonio Carlos Cob-Parro, Yerhard Lalangui and Raquel Lazcano
Agronomy 2024, 14(2), 259; https://doi.org/10.3390/agronomy14020259
Full text available online: https://www.mdpi.com/2073-4395/14/2/259
5. “Weed Management Methods for Herbaceous Field Crops: A Review”
by Wen-Tao Gao and Wen-Hao Su
Agronomy 2024, 14(3), 486; https://doi.org/10.3390/agronomy14030486
Full text available online: https://www.mdpi.com/2073-4395/14/3/486
6. “An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower”
by Luana Centorame, Thomas Gasperini, Alessio Ilari, Andrea Del Gatto and Ester Foppa Pedretti
Agronomy 2024, 14(4), 719; https://doi.org/10.3390/agronomy14040719
Full text available online: https://www.mdpi.com/2073-4395/14/4/719
7. “V-YOLO: A Lightweight and Efficient Detection Model for Guava in Complex Orchard Environments”
by Zhen Liu, Juntao Xiong, Mingrui Cai, Xiaoxin Li and Xinjie Tan
Agronomy 2024, 14(9), 1988; https://doi.org/10.3390/agronomy14091988
Full text available online: https://www.mdpi.com/2073-4395/14/9/1988
8. “Field-Deployed Spectroscopy from 350 to 2500 nm: A Promising Technique for Early Identification of Powdery Mildew Disease (Erysiphe necator) in Vineyards”
by Sergio Vélez, Enrique Barajas, José Antonio Rubio, Dimas Pereira-Obaya and José Ramón Rodríguez-Pérez
Agronomy 2024, 14(3), 634; https://doi.org/10.3390/agronomy14030634
Full text available online: https://www.mdpi.com/2073-4395/14/3/634
9. “Evaluating Time-Series Prediction of Temperature, Relative Humidity, and CO2 in the Greenhouse with Transformer-Based and RNN-Based Models”
by Ju Yeon Ahn, Yoel Kim, Hyeonji Park, Soo Hyun Park and Hyun Kwon Suh
Agronomy 2024, 14(3), 417; https://doi.org/10.3390/agronomy14030417
Full text available online: https://www.mdpi.com/2073-4395/14/3/417
10. “Estimation of Winter Wheat Chlorophyll Content Based on Wavelet Transform and the Optimal Spectral Index”
by Xiaochi Liu, Zhijun Li, Youzhen Xiang, Zijun Tang, Xiangyang Huang, Hongzhao Shi, Tao Sun, Wanli Yang, Shihao Cui, Guofu Chen et al.
Agronomy 2024, 14(6), 1309; https://doi.org/10.3390/agronomy14061309
Full text available online: https://www.mdpi.com/2073-4395/14/6/1309