Xue Qu | Agricultural Resources | Best Researcher Award

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Dr Xue Qu |  Agricultural Resources |  Best Researcher Award

Lecturer at  School of Management/Chengdu University of Information Technology, China

Dr. Xue Qu is a distinguished researcher at the School of Management, Chengdu University of Information Technology. She holds a bachelor’s and master’s degree from China Agricultural University and a doctoral degree from the University of Tokyo, funded by the China Scholarship Council. Her research focuses on agricultural resources, the environment, and food security, with specific interest in food loss and waste, agriculture outsourcing services, and resource and environmental footprints. Dr. Qu has published 12 papers in reputed journals such as Applied Economics and the Journal of Integrative Agriculture. She has participated in several significant projects funded by the Ministry of Agriculture and Rural Affairs and the State Administration of Grain. Dr. Qu’s work offers valuable insights into agricultural outsourcing’s impact on harvest losses, contributing empirical evidence for regulating moral hazards in these services as agricultural marketization expands in the future.

Profile:

🎓 Academic and Professional Background:

Dr. Xue Qu is currently affiliated with the School of Management at Chengdu University of Information Technology. She earned her bachelor’s and master’s degrees from China Agricultural University and her doctoral degree from the University of Tokyo under the China Scholarship Council’s funding. Her research interests span agricultural resources and environment, food security, food loss and waste, agriculture outsourcing service, and resource and environmental footprint. She has published 12 papers in esteemed journals such as Applied Economics, Journal of Integrative Agriculture, and Agriculture.

🔬 Research and Innovations:

Dr. Qu has been involved in several significant research projects, including the “Technology System for Modern Agricultural Industry—Rabbit Industry” funded by the Ministry of Agriculture and Rural Affairs, and “Research on the Investigation and Evaluation Technology of Post-harvest Loss and Waste of Grain” funded by the State Administration of Grain.

📚 Publications and Contributions:

Dr. Qu has contributed significantly to the academic community with her research on rice harvest losses, food security, and agricultural sustainability. Her notable works include articles published in high-impact journals like Applied Economics and Agriculture, where she explores the effects of farming scale, mechanization, and outsourcing services on harvest losses in China. Additionally, she has authored a book chapter on field harvest losses in China.

🏆 Achievements and Recognitions:

With 94 citations on Google Scholar, Dr. Qu’s research has garnered attention and impact in her field. Her work provides empirical evidence on the moral hazards associated with agricultural outsourcing services, a crucial insight as the marketization of agricultural production continues to expand.

🌱 Areas of Research:

  • Food Security
  • Food Economy
  • Agricultural Sustainability

🤝 Collaborations and Memberships:

Dr. Qu collaborates with notable researchers such as Laping Wu, Daizo Kojima, Mitsuyoshi Ando, Yi Luo, Dong Huang, and Fangfang Cao, contributing to advancements in agricultural research.

Publication Top Notes:

  • Can Harvest Outsourcing Services Reduce Field Harvest Losses of Rice in China?
    QU Xue, D Kojima, Y Nishihara, L Wu, A Mitsuyoshi
    Journal of Integrative Agriculture, 20(5), 1396-1406, 2021 (Citations: 39)
  • The Losses in the Rice Harvest Process: A Review
    X Qu, D Kojima, L Wu, M Ando
    Sustainability, 13(17), 9627, 2021 (Citations: 16)
  • Impact of Rice Harvest Loss by Mechanization or Outsourcing: Comparison of Specialized and Part-Time Farmers
    X Qu, D Kojima, Y Nishihara, L Wu, M Ando
    Agricultural Economics/Zemědělská Ekonomika, 66(12), 2020 (Citations: 12)
  • Effects of Different Harvesting Ways on Grain Loss: Based on the Field Survey of 3251 Rural Households in China
    X LI, D HUANG, X QU, J ZHU
    Journal of Natural Resources, 35(5), 1043-1054, 2020 (Citations: 6)
  • Do Farming Scale and Mechanization Affect Moral Hazard in Rice Harvest Outsourcing Service in China?
    X Qu, D Kojima, L Wu, M Ando
    Agriculture, 12(8), 1205, 2022 (Citations: 5)
  • A Study of Rice Harvest Losses in China: Do Mechanization and Farming Scale Matter?
    X Qu, D Kojima, Y Nishihara, L Wu, M Ando
    Japanese Journal of Agricultural Economics, 23, 83-88, 2021 (Citations: 5)
  • Rice Harvest Losses Caused by Agency Slack in China: A Mediation Analysis
    X Qu, D Kojima, L Wu, M Ando
    Applied Economics, 55(10), 1129-1141, 2023 (Citations: 2)
  • An Inverse Relationship between Farm Size and Rice Harvest Loss: Evidence from China
    Y Luo, D Huang, X Qu, L Wu
    Land, 11(10), 1760, 2022 (Citations: 2)
  • Impacts of Work Attitude of Outsourcing Services on Food Losses: Evidence from Rice Harvest in China
    X Qu, D Kojima, L Wu, M Ando
    International Food and Agribusiness Management Review, 25(4), 587-599, 2022 (Citations: 1)
  • Does Outsourcing Skimp Work Attitude? Comparative Analysis between Business and Part-Time Farmers in China
    X Qu, D Kojima, L Wu, M Ando
    Japanese Journal of Farm Management, 60(2), 41-46, 2022 (Citations: 1)
  • Does Outsourcing Skimp Work Attitude?
    Q Xue, K Daizo, W Laping, A Mitsuyoshi
    Agricultural Economics Research, 60(2), 41-46, 2022
  • Harvest Loss Rate of Sweet Potato and Its Influencing Factors
    HY Han Yan, QX Qu Xue, HD Huang Dong, WLP Wu LaPing
    Southwest China Journal of Agricultural Sciences, 2019

 

Alireza Sharifi | Crop yield prediction | Best Researcher Award

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Assoc Prof Dr Alireza Sharifi |  Crop yield prediction |  Best Researcher Award

Dr at  Shahid Rajaee Teacher Training, Iran

Dr. Alireza Sharifi is an Associate Professor at Shahid Rajaee Teacher Training University, specializing in geosciences and remote sensing applications for Precision Agriculture. He obtained his graduate degree from the University of Tehran and has focused his research on Earth Observation Programs, particularly using satellite imagery. Dr. Sharifi has led multiple research projects funded by prestigious organizations such as the National Natural Science Foundation of China, contributing significantly to the fields of hyperspectral image classification, crop mapping, and vegetation monitoring. With numerous publications and a strong academic background, he continues to advance innovative approaches in agricultural sustainability through technology.

Profile:

📚 Academic Background:

Dr. Alireza Sharifi graduated from the University of Tehran with expertise in geosciences and remote sensing applications, focusing on Earth Observation Programs for Precision Agriculture using satellite imagery.

🔍 Research Focus:

His research spans various projects, including hyperspectral image classification, spatio-temporal analysis of forest fires, and integration of satellite images for crop mapping. He has published extensively in reputed journals and holds multiple editorial appointments.

📊 Citations:

  • Citations: 1970 (1878 since 2019)
  • h-index: 28 (since 2019)
  • i10-index: 40 (since 2019)
📄 Publication:

1. Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas
S Barati, B Rayegani, M Saati, A Sharifi, M Nasri
The Egyptian Journal of Remote Sensing and Space Science 14 (1), 49-56, 2011
Citations: 221

2. Hyperspectral image classification using a hybrid 3D-2D convolutional neural networks
S Ghaderizadeh, D Abbasi-Moghadam, A Sharifi, N Zhao, A Tariq
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Citations: 132

3. Yield prediction with machine learning algorithms and satellite images
A Sharifi
Journal of the Science of Food and Agriculture 101 (3), 891-896, 2021
Citations: 105

4. Spatio-temporal analysis of forest fire events in the Margalla Hills, Islamabad, Pakistan using socio-economic and environmental variable data with machine learning methods
A Tariq, H Shu, S Siddiqui, I Munir, A Sharifi, Q Li, L Lu
Journal of Forestry Research 33 (1), 183-194, 2022
Citations: 76

5. Crop type classification by DESIS hyperspectral imagery and machine learning algorithms
N Farmonov, K Amankulova, J Szatmári, A Sharifi, D Abbasi-Moghadam, …
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
Citations: 62

6. Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models
SS Wahla, JH Kazmi, A Sharifi, SA Shirazi, A Tariq, H Joyell Smith
Geocarto International 37 (27), 14963-14982, 2022
Citations: 62

7. Multiscale dual-branch residual spectral–spatial network with attention for hyperspectral image classification
S Ghaderizadeh, D Abbasi-Moghadam, A Sharifi, A Tariq, S Qin
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Citations: 60

8. Evaluation of vegetation indices and phenological metrics using time-series MODIS data for monitoring vegetation change in Punjab, Pakistan
P Hu, A Sharifi, MN Tahir, A Tariq, L Zhang, F Mumtaz, SHIA Shah
Water 13 (18), 2550, 2021
Citations: 59

9. Flash flood susceptibility assessment and zonation by integrating analytic hierarchy process and frequency ratio model with diverse spatial data
A Tariq, J Yan, B Ghaffar, S Qin, BG Mousa, A Sharifi, ME Huq, M Aslam
Water 14 (19), 3069, 2022
Citations: 57

10. Remote sensing satellite’s attitude control system: rapid performance sizing for passive scan imaging mode
A Kosari, A Sharifi, A Ahmadi, M Khoshsima
Aircraft Engineering and Aerospace Technology 92 (7), 1073-1083, 2020
Citations: 57

11. Flood mapping using relevance vector machine and SAR data: A case study from Aqqala, Iran
A Sharifi
Journal of the Indian Society of Remote Sensing 48 (9), 1289-1296, 2020
Citations: 55

12. Modeling and predicting land use land cover spatiotemporal changes: A case study in Chalus Watershed, Iran
S Jalayer, A Sharifi, D Abbasi-Moghadam, A Tariq, S Qin
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Citations: 54

13. Integration of Sentinel 1 and Sentinel 2 satellite images for crop mapping
S Felegari, A Sharifi, K Moravej, M Amin, A Golchin, A Muzirafuti, A Tariq, …
Applied Sciences 11 (21), 10104, 2021
Citations: 54

14. Development of a method for flood detection based on Sentinel‐1 images and classifier algorithms
A Sharifi
Water and Environment Journal 35 (3), 924-929, 2021
Citations: 54

15. Estimation of forest biomass using multivariate relevance vector regression
A Sharifi, J Amini, R Tateishi
Photogrammetric Engineering & Remote Sensing 82 (1), 41-49, 2016
Citations: 54

16. Agro climatic zoning of saffron culture in Miyaneh city by using WLC method and remote sensing data
A Zamani, A Sharifi, S Felegari, A Tariq, N Zhao
Agriculture 12 (1), 118, 2022
Citations: 50

17. Forest biomass estimation using synthetic aperture radar polarimetric features
A Sharifi, J Amini
Journal of Applied Remote Sensing 9 (1), 097695-097695, 2015
Citations: 49

18. Remotely sensed vegetation indices for crop nutrition mapping
A Sharifi
Journal of the Science of Food and Agriculture 100 (14), 5191-5196, 2020
Citations: 47

19. Using Sentinel-2 data to predict nitrogen uptake in maize crop
A Sharifi
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Citations: 47

20. Speckle reduction of PolSAR images in forest regions using fast ICA algorithm
A Sharifi, J Amini, JT Sri Sumantyo, R Tateishi
Journal of the Indian Society of Remote Sensing 43, 339-346, 2015
Citations: 47

 

Guoqiang Dun | Agricultural Machinery | Best Researcher Award

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Dr Guoqiang Dun | Agricultural Machinery | Best Researcher Award

Associate professor at  Intelligent Agricultural Machinery Equipment Engineering Laboratory, Harbin Cambridge College, China

Dr. Guoqiang Dun is a distinguished researcher in the field of intelligent agricultural machinery and equipment, with a focus on intelligent precision control sowing and fertilizing equipment, plot breeding machinery, special vegetable and herb sowing machinery, and computer simulation technology. He has authored over 40 papers in academic journals and holds more than 140 patents. In 2022, he was recognized as the first author in the “Leader 5000 – China’s Top Academic Papers in Excellence Journals (F5000)” and received the first prize in the National Agriculture, Animal Husbandry and Fisheries Harvest Award for Agricultural Technology Promotion Achievement. Dr. Dun has successfully led projects totaling nearly 600,000 RMB.

Profile:

📚 Academic and Professional Background:

Dr. Guoqiang Dun is an expert in intelligent agricultural machinery and equipment, specializing in intelligent precision control sowing and fertilizing equipment, plot breeding machinery, special vegetable and herb sowing machinery, and computer simulation technology. He has published over 40 papers and holds more than 140 patents. In 2022, he was recognized as a leading author in “Leader 5000 – China’s Top Academic Papers in Excellence Journals (F5000)” and received the first prize in the National Agriculture, Animal Husbandry and Fisheries Harvest Award. He has led projects worth nearly 600,000 RMB.

🛠️ Areas of Research:

  • Intelligent agricultural machinery and equipment
  • Intelligent precision control sowing and fertilizing equipment
  • Plot breeding machinery and equipment
  • Special vegetable and herb sowing machinery
  • Computer simulation technology

🚜 Research Focus in Agricultural Machinery:

Guoqiang Dun’s research in agricultural machinery encompasses several key areas: Intelligent Precision Control Sowing and Fertilizing Equipment: Development of advanced sowing and fertilizing machinery with precision control mechanisms. Optimization of fertilizer apparatus using discrete element methods. Plot Breeding Machinery and Equipment: Design and improvement of machinery tailored for plot breeding to enhance efficiency and precision. Innovations in seed-metering wheels and specialized seed discharge devices. Special Vegetable and Herb Sowing Machinery: Creation of specialized machinery for sowing vegetables and herbs with specific requirements. Implementation of unique mechanisms to ensure precise sowing and uniform growth. Computer Simulation Technology: Utilization of computer simulations to optimize machinery design and functionality. Application of software like EDEM and SolidWorks for dynamic simulation and analysis of agricultural processes. Guoqiang Dun’s contributions have significantly advanced the field of agricultural machinery, leading to more efficient, precise, and innovative farming practices.

Citations:

  • Citations: 203
  • Documents Cited: 176
  • Total Documents: 20
  • h-index: 8 (View h-index graph)

Publication Top Notes:

  • Design and Experiment of Side-hung Seed-rowing Spoon Type Precision Seed Metering Device for Radish | 红萝卜侧面悬置排种勺式精量排种器设计与试验
  • Design and Experiment of an Electric Control Spiral-Pushing Feed Mechanism for Field Fertilizer Applicator
  • Optimization and Experiment of the Fertilizer Apparatus with Staggered Gears | 错排齿轮式排肥器优化与试验
  • Simulation Optimization and Experiment of Screw Extrusion Precision Fertilizer Ejector | 螺旋挤压式精量排肥器的仿真优化及试验
  • Optimization Design and Experiment for Precise Control Double Arc Groove Screw Fertilizer Discharger
  • Design and Trajectory Simulation Reliability Analysis of a Self-propelled Strawberry Applicator | 自走式草莓施药机设计与轨迹仿真可靠性分析
  • Optimal Design and Experiment of Corn-Overlapped Strip Fertilizer Spreader
  • Optimization Design and Experiment of Oblique Opening Spiral Precision Control Fertilizer Apparatus | 斜口螺旋精控排肥器优化设计与试验
  • Optimization Design and Experiment of Alternate Post Changing Seed Metering Device for Soybean Plot Breeding | 交替换岗式大豆小区育种排种器优化设计与试验
  • Optimal Design and Experiment of Arc-groove Double-spiral Fertilizer Discharge Device | 弧槽双螺旋式排肥器优化设计与试验

 

 

Jianguo Wu | Agricultural Intelligent Equipment | Best Researcher Award

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Mr Jianguo Wu |  Agricultural Intelligent Equipment | Best Researcher Award

Postgraduate at  Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, China

Jianguo Wu, a graduate student specializing in agricultural intelligent equipment, is currently affiliated with the Precision Pesticide Application Department at the Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences. Born on March 16, 1995, Jianguo began his master’s degree in Mechanical Engineering at the College of Mechanical and Electrical Engineering, Xinjiang Agricultural University in September 2021. His academic journey led him to joint training at the Intelligent Equipment Research Center from June 2022 to June 2024, focusing on designing a spray boom height control system for sprayers.

 

Profile:

Educational Journey:

🎓 Master’s in Mechanical Engineering from the College of Mechanical and Electrical Engineering, Xinjiang Agricultural University (commenced in September 2021). 📚 Joint Training at the Intelligent Equipment Research Center from June 2022 to June 2024.

Professional Experience:

📝 Published two core papers in Chinese. 🌐 Published one JCR Q1 paper. 🏆 Holder of a Chinese invention patent and a computer software copyright.

Achievements:

  • 📈 H-index and Cumulative Impact Factor: 6.342.
  • 🏅 Received 4 awards and recognitions.
  • 👥 Active member of 40 professional bodies.

Jianguo Wu’s research and innovations significantly contribute to advancing agricultural intelligent equipment technology, making strides toward the future of autonomous farming. 🌾🔧

Research Focus in Agricultural Intelligent Equipment:

Jianguo Wu’s research focuses on advancing Agricultural Intelligent Equipment, particularly specializing in the precision and efficiency of pesticide application. His work, based at the Intelligent Equipment Research Center of the Beijing Academy of Agriculture and Forestry Sciences, centers on developing innovative systems for spray boom height control in agricultural sprayers. Through his expertise, Jianguo aims to enhance the automation and effectiveness of farming practices, contributing to sustainable agricultural solutions and improved crop yield management.

Publication:

Model for Detecting Boom Height Based on an Ultrasonic Sensor for the Whole Growth Cycle of Wheat Agriculture, 2023-12-22 | Journal Article DOI: 10.3390/agriculture14010021