Idan Kopler | Precision Livestock Farming | Research Excellence Award

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Dr. Idan Kopler | Precision Livestock Farming | Research Excellence Award

Project manager | MIGAL Galilee Research Institute Ltd  | Israel

Idan Kopler is an interdisciplinary environmental researcher specializing in ecosystem ecology, hydrology, climate–vegetation interactions, and data-driven agri-environmental systems. His research focus centers on understanding how temperature, drought, and soil moisture regulate ecosystem processes across forested and managed landscapes, with emerging interests in the socio-environmental dimensions of precision livestock farming and technology adoption. Kopler has held research-oriented academic roles, contributing to cross-sector collaborations that bridge ecological science with applied agricultural systems. His key contributions include advancing empirical evidence on growth-limiting climatic drivers at montane treelines, quantifying soil–vegetation–water interactions in Mediterranean forests, and evaluating farmer perceptions of benefits and risks associated with precision livestock farming technologies in the European context. Through integrative field measurements, modeling, and stakeholder-focused analyses, his work supports evidence-based decision-making. Kopler’s impact vision is to strengthen climate-resilient land and livestock systems by aligning ecological insight with practical innovation, informing sustainable management, policy dialogue, and responsible technological adoption across diverse environments.

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Featured Publications

Muhammad Faheem | Robotics in Agriculture | Editorial Board Member

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Dr. Muhammad Faheem | Robotics in Agriculture | Editorial Board Member

Assistant Professor | University of Agriculture | Pakistan

Dr. Muhammad Faheem is a distinguished researcher specializing in Agricultural Mechanization, Robotics in Agriculture, Intelligent Automation, and Control Engineering, with a strong body of work advancing smart and sustainable farming technologies. His research focuses on developing autonomous systems, machine-vision tools, and sensor-based intelligent solutions that enhance precision agriculture, optimize resource efficiency, and improve post-harvest handling processes, with emerging interests in deep-learning applications, multisensory navigation, and automated crop-quality assessment. With key academic appointments, including his role as Assistant Professor at the University of Agriculture Faisalabad and research affiliation with Jiangsu University, China, he has contributed extensively to engineering innovative systems for real-time agricultural problem-solving. Dr. Faheem’s major contributions include pioneering CNN-based defect detection frameworks for photovoltaic cells, intelligent sensor-guided variable-rate spraying systems, and advanced autonomous navigation technologies for multi-crop field environments; he has also significantly advanced post-harvest robotics through novel vibration-reduction mechanisms and fruit-handling models, in addition to impactful work on sustainable technologies such as smart composting, biogas systems, and solar thermal collectors. His research has influenced diverse areas including climate-responsive agricultural management, disease-detection systems using deep learning, and high-precision fruit-picking robotics—contributions reflected in widely cited publications across leading journals in energy engineering, environmental sciences, precision agriculture, and smart automation. Dr. Faheem’s impact vision centers on accelerating the global transition toward intelligent, autonomous, and environmentally responsible farming systems by integrating robotics, machine learning, and sensor fusion into practical agricultural operations. Through his interdisciplinary work, he aims to contribute to resilient food production, reduced environmental footprints, and enhanced technological adoption in agriculture worldwide, ensuring that innovations in automation and sustainability directly benefit farmers, industry stakeholders, and the broader scientific community.

Profile: Google Scholar 

Featured Publications 

1. Akram, M. W., Li, G., Jin, Y., Chen, X., Zhu, C., Zhao, X., Khaliq, A., Faheem, M., et al. (2019). CNN-based automatic detection of photovoltaic cell defects in electroluminescence images. Energy, 189, 116319.

2. Abbas, I., Liu, J., Faheem, M., Noor, R. S., Shaikh, S. A., Solangi, K. A., & Raza, S. M. (2020). Different sensor-based intelligent spraying systems in agriculture. Sensors and Actuators A: Physical, 316, 112265.

3. Xie, B., Jin, Y., Faheem, M., Gao, W., Liu, J., Jiang, H., Cai, L., & Li, Y. (2023). Research progress of autonomous navigation technology for multi-agricultural scenes. Computers and Electronics in Agriculture, 211, 107963.

4. Javed, T., Afzal, I., Shabbir, R., Ikram, K., Zaheer, M. S., Faheem, M., Ali, H. H., et al. (2022). Seed coating technology: An innovative and sustainable approach for improving seed quality and crop performance. Journal of the Saudi Society of Agricultural Sciences, 21(8), 536–545.

5. Ajmal, M., Shi, A., Awais, M., Mengqi, Z., Zihao, X., Shabbir, A., Faheem, M., et al. (2021). Ultra-high temperature aerobic fermentation pretreatment composting: Parameters optimization, mechanisms and compost quality assessment. Journal of Environmental Chemical Engineering, 9(4), 105453.

 

Assoc. Prof. Dr. Sinan Demir | Precision Farming | Best Researcher Award 

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Assoc. Prof. Dr. Sinan Demir | Precision Farming | Best Researcher Award 

Assistant Professor | Isparta University of Applied Sciences | Turkey

Dr. Sinan Demir is an Assistant Professor at Isparta University of Applied Sciences, within the Faculty of Agriculture, specializing in Soil Science and Plant Nutrition. He earned his PhD from Isparta University of Applied Sciences in 2021, focusing on the use of unmanned aerial vehicles (UAVs) for monitoring oil rose (Rosa damascena mill.) gardens and developing yield prediction models. Prior to that, he completed a Master’s degree at Suleyman Demirel University, where he researched high-resolution satellite imagery applications for detecting poppy (Papaver somniferum) cultivation areas. His academic and research pursuits concentrate on digital agriculture, remote sensing, hyperspectral imaging, geographic information systems (GIS), and machine learning applications in agriculture. Dr. Demir has made significant contributions through publications in prestigious journals such as Environment, Development and Sustainability; Natural Hazards; Remote Sensing Applications: Society and Environment; and Trees, Forests and People. His work includes advanced studies on erosion assessment using Google Earth Engine, yield prediction through UAV multispectral imagery, and digital mapping of burn severity. He has authored several book chapters, notably on machine learning algorithms and remote sensing applications in agriculture. Dr. Demir actively participates in national and international research projects, including TUBITAK-supported initiatives focusing on hyperspectral data applications in agriculture and soil property classification using machine learning techniques. His editorial responsibilities include serving as an editor for the journal "Veri Bilimi". In addition to his research, Dr. Demir plays an important role in shaping future agricultural scientists by teaching courses such as Remote Sensing and GIS, Digital Agriculture, Hyperspectral Imaging Techniques, and Integration of UAV Data in Geographic Systems at undergraduate, master’s, and doctoral levels. His contributions have been recognized through competitive scholarships like the TÜBİTAK Doctoral Scholarship Program, and he has held multiple jury memberships for thesis evaluations. Dr. Demir’s commitment to advancing sustainable agricultural practices and innovative research methodologies positions him as a notable researcher in his field.

Profiles: Google Scholar | ORCID | Scopus

Featured Publications

1.Alaboz, P., Dengiz, O., Demir, S., & Şenol, H. (2021). Digital mapping of soil erodibility factors based on decision tree using geostatistical approaches in terrestrial ecosystem. Catena.
Cited: 60 times

2. Şenol, H., Alaboz, P., Demir, S., & Dengiz, O. (2020). Computational intelligence applied to soil quality index using GIS and geostatistical approaches in semiarid ecosystem. Arabian Journal of Geosciences.
Cited: 50 times

3. Demir, S., & Dursun, I. (2024). Assessment of pre-and post-fire erosion using the RUSLE equation in a watershed affected by the forest fire on Google Earth Engine: the study of Manavgat River Basin. Natural Hazards.
Cited: 28 times

4. Alaboz, P., Demir, S., & Dengiz, O. (2021). Assessment of various pedotransfer functions for the prediction of the dry bulk density of cultivated soils in a semiarid environment. Communications in Soil Science and Plant Analysis.
Cited: 25 times

5. Alaboz, P., Demir, S., & Dengiz, O. (2020). Determination of spatial distribution of soil moisture constant using different interpolation model case study, Isparta Atabey Plain. Journal of Tekirdag Agricultural Faculty.
Cited: 23 times