Agronomy | Highly Cited Papers in 2023–2024 in the “Precision and Digital Agriculture” Section
The “Precision and Digital Agriculture” Section of Agronomy (ISSN: 2073-4395) aims to improve the effectiveness, sustainability, and resilience of crop production and agronomic processes, a variety of digital and precision technologies—such as AI, IoT, deep learning, imaging, and machine learning—are being integrated into key areas such as soil and nutrient management, agronomic crop protection, plant breeding, post-harvest handling, and resource optimization. This section highlights research that translates these innovations into practical solutions for crop and yield prediction, production, and agronomic management. Preference is given to studies where digital and precision technologies are applied within the context of crop science and agronomic systems, rather than those focused primarily on algorithm development, robotics design, or hardware engineering, without clear agricultural relevance. Focused on bridging cutting-edge advancements in digital agriculture with real-world agronomic practices, this section welcomes the submission of original research, reviews, mini-reviews, perspectives, and methodology papers that support sustainable intensification, environmental stewardship, and greater resilience in crop production systems.
You have free and unlimited access to the full texts of all of the open-access articles published in our journal. We welcome you to read our most highly cited papers published in 2023–2024, listed below:
1. “A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention”
by Guoliang Yang, Jixiang Wang, Ziling Nie, Hao Yang and Shuaiying Yu
Agronomy 2023, 13(7), 1824; https://doi.org/10.3390/agronomy13071824
Full text available online: http://www.mdpi.com/2073-4395/13/7/1824
2. “Fruit Detection and Recognition Based on Deep Learning for Automatic Harvesting: An Overview and Review”
by Feng Xiao, Haibin Wang, Yueqin Xu and Ruiqing Zhang
Agronomy 2023, 13(6), 1625; https://doi.org/10.3390/agronomy13061625
Full text available online: http://www.mdpi.com/2073-4395/13/6/1625
3. “Comparing Inception V3, VGG 16, VGG 19, CNN, and ResNet 50: A Case Study on Early Detection of a Rice Disease”
by Syed Rehan Shah, Salman Qadri, Hadia Bibi, Syed Muhammad Waqas Shah, Muhammad Imran Sharif and Francesco Marinello
Agronomy 2023, 13(6), 1633; https://doi.org/10.3390/agronomy13061633
Full text available online: http://www.mdpi.com/2073-4395/13/6/1633
4. “Artificial Intelligence: Implications for the Agri-Food Sector”
by Akriti Taneja, Gayathri Nair, Manisha Joshi, Somesh Sharma, Surabhi Sharma, Anet Rezek Jambrak, Elena Roselló-Soto, Francisco J. Barba, Juan M. Castagnini, Noppol Leksawasdi and Yuthana Phimolsiripol
Agronomy 2023, 13(5), 1397; https://doi.org/10.3390/agronomy13051397
Full text available online: http://www.mdpi.com/2073-4395/13/5/1397
5. “Research on Apple Object Detection and Localization Method Based on Improved YOLOX and RGB-D Images”
by Tiantian Hu, Wenbo Wang, Jinan Gu, Zilin Xia, Jian Zhang and Bo Wang
Agronomy 2023, 13(7), 1816; https://doi.org/10.3390/agronomy13071816
Full text available online: http://www.mdpi.com/2073-4395/13/7/1816
6. “Applying IoT Sensors and Big Data to Improve Precision Crop Production: A Review”
by Tarek Alahmad, Miklós Neményi and Anikó Nyéki
Agronomy 2023, 13(10), 2603; https://doi.org/10.3390/agronomy13102603
Full text available online: http://www.mdpi.com/2073-4395/13/10/2603
7. “Detection and Counting of Small Target Apples under Complicated Environments by Using Improved YOLOv7-tiny”
by Li Ma, Liya Zhao, Zixuan Wan, Jian Zhang and Guifen Chen
Agronomy 2023, 13(5), 1419; https://doi.org/10.3390/agronomy13051419
Full text available online: http://www.mdpi.com/2073-4395/13/5/1419
8. “TS-YOLO: An All-Day and Lightweight Tea Canopy Shoots Detection Model”
by Zhi Zhang, Yongzong Lu, Yiqiu Zhao, Qingmin Pan, Kuang Jin, Gang Xu and Yongguang Hu
Agronomy 2023, 13(5), 1411; https://doi.org/10.3390/agronomy13051411
Full text available online: http://www.mdpi.com/2073-4395/13/5/1411
9. “Row Detection BASED Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields: Methods and Applications”
by Jiayou Shi, Yuhao Bai, Zhihua Diao, Jun Zhou, Xingbo Yao and Baohua Zhang
Agronomy 2023, 13(7), 1780; https://doi.org/10.3390/agronomy13071780
Full text available online: http://www.mdpi.com/2073-4395/13/7/1780
10. “Multi-Stage Corn Yield Prediction Using High-Resolution UAV Multispectral Data and Machine Learning Models”
by Chandan Kumar, Partson Mubvumba, Yanbo Huang, Jagman Dhillon and Krishna Reddy
Agronomy 2023, 13(5), 1277; https://doi.org/10.3390/agronomy13051277
Full text available online: http://www.mdpi.com/2073-4395/13/5/1277
11. “Estimation of Relative Chlorophyll Content in Spring Wheat Based on Multi-Temporal UAV Remote Sensing”
by Qiang Wu, Yongping Zhang, Zhiwei Zhao, Min Xie and Dingyi Hou
Agronomy 2023, 13(1), 211; https://doi.org/10.3390/agronomy13010211
Full text available online: http://www.mdpi.com/2073-4395/13/1/211
12. “Identification Method of Corn Leaf Disease Based on Improved Mobilenetv3 Model”
by Chunguang Bi, Suzhen Xu, Nan Hu, Shuo Zhang, Zhenyi Zhu and Helong Yu
Agronomy 2023, 13(2), 300; https://doi.org/10.3390/agronomy13020300
Full text available online: http://www.mdpi.com/2073-4395/13/2/300
13. “DCF-Yolov8: An Improved Algorithm for Aggregating Low-Level Features to Detect Agricultural Pests and Diseases”
by Lijuan Zhang, Gongcheng Din, Chaoran Li and Dongming Li
Agronomy 2023, 13(8), 2012; https://doi.org/10.3390/agronomy13082012
Full text available online: http://www.mdpi.com/2073-4395/13/8/2012
14. “ViT-SmartAgri: Vision Transformer and Smartphone-Based Plant Disease Detection for Smart Agriculture”
by Utpal Barman , Parismita Sarma, Mirzanur Rahman, Vaskar Deka, Swati Lahkar, Vaishali Sharma and Manob Jyoti Saikia
Agronomy 2024, 14(2), 327; https://doi.org/10.3390/agronomy14020327
Full text available online: http://www.mdpi.com/2073-4395/14/2/327
15. “BMAE-Net: A Data-Driven Weather Prediction Network for Smart Agriculture”
by Jian-Lei Kong, Xiao-Meng Fan, Xue-Bo Jin, Ting-Li Su, Yu-Ting Bai, Hui-Jun Ma and Min Zuo
Agronomy 2023, 13(3), 625; https://doi.org/10.3390/agronomy13030625
Full text available online: http://www.mdpi.com/2073-4395/13/3/625
16. “RDE-YOLOv7: An Improved Model Based on YOLOv7 for Better Performance in Detecting Dragon Fruits”
by Jialiang Zhou, Yueyue Zhang and Jinpeng Wang
Agronomy 2023, 13(4), 1042; https://doi.org/10.3390/agronomy13041042
Full text available online: http://www.mdpi.com/2073-4395/13/4/1042
17. “Improving Deep Learning Classifiers Performance via Preprocessing and Class Imbalance Approaches in a Plant Disease Detection Pipeline”
by Mike O. Ojo and Azlan Zahid
Agronomy 2023, 13(3), 887; https://doi.org/10.3390/agronomy13030887
Full text available online: http://www.mdpi.com/2073-4395/13/3/887
18. “Realtime Picking Point Decision Algorithm of Trellis Grape for High-Speed Robotic Cut-and-Catch Harvesting”
by Zhujie Xu, Jizhan Liu, Jie Wang, Lianjiang Cai, Yucheng Jin, Shengyi Zhao and Binbin Xie
Agronomy 2023, 13(6), 1618; https://doi.org/10.3390/agronomy13061618
Full text available online: http://www.mdpi.com/2073-4395/13/6/1618
19. “Research on Instance Segmentation Algorithm of Greenhouse Sweet Pepper Detection Based on Improved Mask RCNN”
by Peichao Cong, Shanda Li, Jiachao Zhou, Kunfeng Lv and Hao Feng
Agronomy 2023, 13(1), 196; https://doi.org/10.3390/agronomy13010196
Full text available online: http://www.mdpi.com/2073-4395/13/1/196
20. “Complementary Use of Ground-Based Proximal Sensing and Airborne/Spaceborne Remote Sensing Techniques in Precision Agriculture: A Systematic Review”
by Angelos Alexopoulos, Konstantinos Koutras, Sihem Ben Ali, Stefano Puccio, Alessandro Carella, Roberta Ottaviano and Athanasios Kalogeras
Agronomy 2023, 13(7), 1942; https://doi.org/10.3390/agronomy13071942
Full text available online: http://www.mdpi.com/2073-4395/13/7/1942
21. “A Lightweight Cherry Tomato Maturity Real-Time Detection Algorithm Based on Improved YOLOV5n”
by Congyue Wang, Chaofeng Wang, Lele Wang, Jing Wang, Jiapeng Liao, Yuanhong Li and Yubin Lan
Agronomy 2023, 13(8), 2106; https://doi.org/10.3390/agronomy13082106
Full text available online: http://www.mdpi.com/2073-4395/13/8/2106
22. “Edge Device Detection of Tea Leaves with One Bud and Two Leaves Based on ShuffleNetv2-YOLOv5-Lite-E”
by Shihao Zhang, Hekai Yang, Chunhua Yang, Wenxia Yuan, Xinghui Li, Xinghua Wang, Yinsong Zhang, Xiaobo Cai, Yubo Sheng, Xiujuan Deng et al.
Agronomy 2023, 13(2), 577; https://doi.org/10.3390/agronomy13020577
Full text available online: http://www.mdpi.com/2073-4395/13/2/577
23. “Remote Sensing Monitoring of Rice Diseases and Pests from Different Data Sources: A Review”
by Qiong Zheng, Wenjiang Huang, Qing Xia, Yingying Dong, Huichun Ye, Hao Jiang, Shuisen Chen and Shanyu Huang
Agronomy 2023, 13(7), 1851; https://doi.org/10.3390/agronomy13071851
Full text available online: http://www.mdpi.com/2073-4395/13/7/1851
24. “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: http://www.mdpi.com/2073-4395/14/2/363
25. “An Automatic Jujube Fruit Detection and Ripeness Inspection Method in the Natural Environment”
by Defang Xu, Huamin Zhao, Olarewaju Mubashiru Lawal, Xinyuan Lu, Rui Ren and Shujuan Zhang
Agronomy 2023, 13(2), 451; https://doi.org/10.3390/agronomy13020451
Full text available online: http://www.mdpi.com/2073-4395/13/2/451