Samsuzzaman | Agricultural Machinery Engineering | Best Researcher Award Published on 11/11/202511/11/2025 by Environmental Scientists Dr. Samsuzzaman | Agricultural Machinery Engineering | Best Researcher Award Chungnam National University | South Korea Dr. Samsuzzaman is an emerging researcher in the field of smart and precision agriculture, focusing on the integration of artificial intelligence, sensor fusion, and digital technologies for sustainable crop and livestock production. His research interests span AI-based smart farming, controlled environment agriculture (CEA), vertical farming, remote sensing in agriculture, and digital livestock farming. His scientific contributions include several high-impact publications in international journals such as Agronomy, Horticulturae, Precision Agriculture, and the Journal of Animal Science and Technology, addressing topics like stress detection in seedlings, plant factory automation, and computer vision applications in livestock systems. His work demonstrates innovative use of machine learning, deep learning, and image processing to enhance environmental monitoring and resource efficiency in agricultural systems. With 181 citations, an h-index of 7, and an i10-index of 4, his research continues to gain recognition globally. Recognized with multiple academic honors, including Best Oral and Poster Presentation Awards from leading Korean scientific societies, Dr. Samsuzzaman actively contributes to interdisciplinary research projects funded by the Ministry of Agriculture, Food and Rural Affairs, South Korea. His recent studies on sensor fusion and machine vision for real-time crop stress quantification are pioneering steps toward next-generation precision agriculture technologies. As a member of international professional societies such as ASABE, ISPA, KSAM, and BKS, he is dedicated to advancing smart farming innovations that promote agricultural sustainability, productivity, and climate resilience through the convergence of automation, data analytics, and environmental intelligence. Profiles: Google Scholar | Orcid Featured Publications Kabir, M. S. N., Reza, M. N., Chowdhury, M., Ali, M., Samsuzzaman, Ali, M. R., & others. (2023). Technological trends and engineering issues on vertical farms: A review. Horticulturae, 9(11), 1229. Reza, M. N., Ali, M. R., Kabir, M. S. N., Karim, M. R., Ahmed, S., Kyoung, H., Kim, G., & Samsuzzaman. (2024). Thermal imaging and computer vision technologies for the enhancement of pig husbandry: A review. Journal of Animal Science and Technology, 66(1), 31. Islam, S., Reza, M. N., Samsuzzaman, S. A., Cho, Y. J., Noh, D. H., Chung, S. O., & others. (2024). Machine vision and artificial intelligence for plant growth stress detection and monitoring: A review. Precision Agriculture, 6(1), 34. Islam, S., Samsuzzaman, Reza, M. N., Lee, K. H., Ahmed, S., Cho, Y. J., Noh, D. H., & others. (2024). Image processing and support vector machine (SVM) for classifying environmental stress symptoms of pepper seedlings grown in a plant factory. Agronomy, 14(9), 2043. Islam, S., Reza, M. N., Ahmed, S., Samsuzzaman, Lee, K. H., Cho, Y. J., Noh, D. H., & others. (2024). Nutrient stress symptom detection in cucumber seedlings using segmented regression and a mask region-based convolutional neural network model. Agriculture, 14(8), 1390.*
Dr. Samsuzzaman is an emerging researcher in the field of smart and precision agriculture, focusing on the integration of artificial intelligence, sensor fusion, and digital technologies for sustainable crop and livestock production. His research interests span AI-based smart farming, controlled environment agriculture (CEA), vertical farming, remote sensing in agriculture, and digital livestock farming. His scientific contributions include several high-impact publications in international journals such as Agronomy, Horticulturae, Precision Agriculture, and the Journal of Animal Science and Technology, addressing topics like stress detection in seedlings, plant factory automation, and computer vision applications in livestock systems. His work demonstrates innovative use of machine learning, deep learning, and image processing to enhance environmental monitoring and resource efficiency in agricultural systems. With 181 citations, an h-index of 7, and an i10-index of 4, his research continues to gain recognition globally. Recognized with multiple academic honors, including Best Oral and Poster Presentation Awards from leading Korean scientific societies, Dr. Samsuzzaman actively contributes to interdisciplinary research projects funded by the Ministry of Agriculture, Food and Rural Affairs, South Korea. His recent studies on sensor fusion and machine vision for real-time crop stress quantification are pioneering steps toward next-generation precision agriculture technologies. As a member of international professional societies such as ASABE, ISPA, KSAM, and BKS, he is dedicated to advancing smart farming innovations that promote agricultural sustainability, productivity, and climate resilience through the convergence of automation, data analytics, and environmental intelligence. Profiles: Google Scholar | Orcid
Dr. Samsuzzaman is an emerging researcher in the field of smart and precision agriculture, focusing on the integration of artificial intelligence, sensor fusion, and digital technologies for sustainable crop and livestock production. His research interests span AI-based smart farming, controlled environment agriculture (CEA), vertical farming, remote sensing in agriculture, and digital livestock farming. His scientific contributions include several high-impact publications in international journals such as Agronomy, Horticulturae, Precision Agriculture, and the Journal of Animal Science and Technology, addressing topics like stress detection in seedlings, plant factory automation, and computer vision applications in livestock systems. His work demonstrates innovative use of machine learning, deep learning, and image processing to enhance environmental monitoring and resource efficiency in agricultural systems. With 181 citations, an h-index of 7, and an i10-index of 4, his research continues to gain recognition globally. Recognized with multiple academic honors, including Best Oral and Poster Presentation Awards from leading Korean scientific societies, Dr. Samsuzzaman actively contributes to interdisciplinary research projects funded by the Ministry of Agriculture, Food and Rural Affairs, South Korea. His recent studies on sensor fusion and machine vision for real-time crop stress quantification are pioneering steps toward next-generation precision agriculture technologies. As a member of international professional societies such as ASABE, ISPA, KSAM, and BKS, he is dedicated to advancing smart farming innovations that promote agricultural sustainability, productivity, and climate resilience through the convergence of automation, data analytics, and environmental intelligence. Profiles: Google Scholar | Orcid
Dr. Samsuzzaman is an emerging researcher in the field of smart and precision agriculture, focusing on the integration of artificial intelligence, sensor fusion, and digital technologies for sustainable crop and livestock production. His research interests span AI-based smart farming, controlled environment agriculture (CEA), vertical farming, remote sensing in agriculture, and digital livestock farming. His scientific contributions include several high-impact publications in international journals such as Agronomy, Horticulturae, Precision Agriculture, and the Journal of Animal Science and Technology, addressing topics like stress detection in seedlings, plant factory automation, and computer vision applications in livestock systems. His work demonstrates innovative use of machine learning, deep learning, and image processing to enhance environmental monitoring and resource efficiency in agricultural systems. With 181 citations, an h-index of 7, and an i10-index of 4, his research continues to gain recognition globally. Recognized with multiple academic honors, including Best Oral and Poster Presentation Awards from leading Korean scientific societies, Dr. Samsuzzaman actively contributes to interdisciplinary research projects funded by the Ministry of Agriculture, Food and Rural Affairs, South Korea. His recent studies on sensor fusion and machine vision for real-time crop stress quantification are pioneering steps toward next-generation precision agriculture technologies. As a member of international professional societies such as ASABE, ISPA, KSAM, and BKS, he is dedicated to advancing smart farming innovations that promote agricultural sustainability, productivity, and climate resilience through the convergence of automation, data analytics, and environmental intelligence. Profiles: Google Scholar | Orcid
Dr. Samsuzzaman is an emerging researcher in the field of smart and precision agriculture, focusing on the integration of artificial intelligence, sensor fusion, and digital technologies for sustainable crop and livestock production. His research interests span AI-based smart farming, controlled environment agriculture (CEA), vertical farming, remote sensing in agriculture, and digital livestock farming. His scientific contributions include several high-impact publications in international journals such as Agronomy, Horticulturae, Precision Agriculture, and the Journal of Animal Science and Technology, addressing topics like stress detection in seedlings, plant factory automation, and computer vision applications in livestock systems. His work demonstrates innovative use of machine learning, deep learning, and image processing to enhance environmental monitoring and resource efficiency in agricultural systems. With 181 citations, an h-index of 7, and an i10-index of 4, his research continues to gain recognition globally. Recognized with multiple academic honors, including Best Oral and Poster Presentation Awards from leading Korean scientific societies, Dr. Samsuzzaman actively contributes to interdisciplinary research projects funded by the Ministry of Agriculture, Food and Rural Affairs, South Korea. His recent studies on sensor fusion and machine vision for real-time crop stress quantification are pioneering steps toward next-generation precision agriculture technologies. As a member of international professional societies such as ASABE, ISPA, KSAM, and BKS, he is dedicated to advancing smart farming innovations that promote agricultural sustainability, productivity, and climate resilience through the convergence of automation, data analytics, and environmental intelligence. Profiles: Google Scholar | Orcid
Dr. Samsuzzaman is an emerging researcher in the field of smart and precision agriculture, focusing on the integration of artificial intelligence, sensor fusion, and digital technologies for sustainable crop and livestock production. His research interests span AI-based smart farming, controlled environment agriculture (CEA), vertical farming, remote sensing in agriculture, and digital livestock farming. His scientific contributions include several high-impact publications in international journals such as Agronomy, Horticulturae, Precision Agriculture, and the Journal of Animal Science and Technology, addressing topics like stress detection in seedlings, plant factory automation, and computer vision applications in livestock systems. His work demonstrates innovative use of machine learning, deep learning, and image processing to enhance environmental monitoring and resource efficiency in agricultural systems. With 181 citations, an h-index of 7, and an i10-index of 4, his research continues to gain recognition globally. Recognized with multiple academic honors, including Best Oral and Poster Presentation Awards from leading Korean scientific societies, Dr. Samsuzzaman actively contributes to interdisciplinary research projects funded by the Ministry of Agriculture, Food and Rural Affairs, South Korea. His recent studies on sensor fusion and machine vision for real-time crop stress quantification are pioneering steps toward next-generation precision agriculture technologies. As a member of international professional societies such as ASABE, ISPA, KSAM, and BKS, he is dedicated to advancing smart farming innovations that promote agricultural sustainability, productivity, and climate resilience through the convergence of automation, data analytics, and environmental intelligence. Profiles: Google Scholar | Orcid
Dr. Samsuzzaman is an emerging researcher in the field of smart and precision agriculture, focusing on the integration of artificial intelligence, sensor fusion, and digital technologies for sustainable crop and livestock production. His research interests span AI-based smart farming, controlled environment agriculture (CEA), vertical farming, remote sensing in agriculture, and digital livestock farming. His scientific contributions include several high-impact publications in international journals such as Agronomy, Horticulturae, Precision Agriculture, and the Journal of Animal Science and Technology, addressing topics like stress detection in seedlings, plant factory automation, and computer vision applications in livestock systems. His work demonstrates innovative use of machine learning, deep learning, and image processing to enhance environmental monitoring and resource efficiency in agricultural systems. With 181 citations, an h-index of 7, and an i10-index of 4, his research continues to gain recognition globally. Recognized with multiple academic honors, including Best Oral and Poster Presentation Awards from leading Korean scientific societies, Dr. Samsuzzaman actively contributes to interdisciplinary research projects funded by the Ministry of Agriculture, Food and Rural Affairs, South Korea. His recent studies on sensor fusion and machine vision for real-time crop stress quantification are pioneering steps toward next-generation precision agriculture technologies. As a member of international professional societies such as ASABE, ISPA, KSAM, and BKS, he is dedicated to advancing smart farming innovations that promote agricultural sustainability, productivity, and climate resilience through the convergence of automation, data analytics, and environmental intelligence. Profiles: Google Scholar | Orcid
Dr. Samsuzzaman is an emerging researcher in the field of smart and precision agriculture, focusing on the integration of artificial intelligence, sensor fusion, and digital technologies for sustainable crop and livestock production. His research interests span AI-based smart farming, controlled environment agriculture (CEA), vertical farming, remote sensing in agriculture, and digital livestock farming. His scientific contributions include several high-impact publications in international journals such as Agronomy, Horticulturae, Precision Agriculture, and the Journal of Animal Science and Technology, addressing topics like stress detection in seedlings, plant factory automation, and computer vision applications in livestock systems. His work demonstrates innovative use of machine learning, deep learning, and image processing to enhance environmental monitoring and resource efficiency in agricultural systems. With 181 citations, an h-index of 7, and an i10-index of 4, his research continues to gain recognition globally. Recognized with multiple academic honors, including Best Oral and Poster Presentation Awards from leading Korean scientific societies, Dr. Samsuzzaman actively contributes to interdisciplinary research projects funded by the Ministry of Agriculture, Food and Rural Affairs, South Korea. His recent studies on sensor fusion and machine vision for real-time crop stress quantification are pioneering steps toward next-generation precision agriculture technologies. As a member of international professional societies such as ASABE, ISPA, KSAM, and BKS, he is dedicated to advancing smart farming innovations that promote agricultural sustainability, productivity, and climate resilience through the convergence of automation, data analytics, and environmental intelligence. Profiles: Google Scholar | Orcid
Dr. Samsuzzaman is an emerging researcher in the field of smart and precision agriculture, focusing on the integration of artificial intelligence, sensor fusion, and digital technologies for sustainable crop and livestock production. His research interests span AI-based smart farming, controlled environment agriculture (CEA), vertical farming, remote sensing in agriculture, and digital livestock farming. His scientific contributions include several high-impact publications in international journals such as Agronomy, Horticulturae, Precision Agriculture, and the Journal of Animal Science and Technology, addressing topics like stress detection in seedlings, plant factory automation, and computer vision applications in livestock systems. His work demonstrates innovative use of machine learning, deep learning, and image processing to enhance environmental monitoring and resource efficiency in agricultural systems. With 181 citations, an h-index of 7, and an i10-index of 4, his research continues to gain recognition globally. Recognized with multiple academic honors, including Best Oral and Poster Presentation Awards from leading Korean scientific societies, Dr. Samsuzzaman actively contributes to interdisciplinary research projects funded by the Ministry of Agriculture, Food and Rural Affairs, South Korea. His recent studies on sensor fusion and machine vision for real-time crop stress quantification are pioneering steps toward next-generation precision agriculture technologies. As a member of international professional societies such as ASABE, ISPA, KSAM, and BKS, he is dedicated to advancing smart farming innovations that promote agricultural sustainability, productivity, and climate resilience through the convergence of automation, data analytics, and environmental intelligence. Profiles: Google Scholar | Orcid
Dr. Samsuzzaman is an emerging researcher in the field of smart and precision agriculture, focusing on the integration of artificial intelligence, sensor fusion, and digital technologies for sustainable crop and livestock production. His research interests span AI-based smart farming, controlled environment agriculture (CEA), vertical farming, remote sensing in agriculture, and digital livestock farming. His scientific contributions include several high-impact publications in international journals such as Agronomy, Horticulturae, Precision Agriculture, and the Journal of Animal Science and Technology, addressing topics like stress detection in seedlings, plant factory automation, and computer vision applications in livestock systems. His work demonstrates innovative use of machine learning, deep learning, and image processing to enhance environmental monitoring and resource efficiency in agricultural systems. With 181 citations, an h-index of 7, and an i10-index of 4, his research continues to gain recognition globally. Recognized with multiple academic honors, including Best Oral and Poster Presentation Awards from leading Korean scientific societies, Dr. Samsuzzaman actively contributes to interdisciplinary research projects funded by the Ministry of Agriculture, Food and Rural Affairs, South Korea. His recent studies on sensor fusion and machine vision for real-time crop stress quantification are pioneering steps toward next-generation precision agriculture technologies. As a member of international professional societies such as ASABE, ISPA, KSAM, and BKS, he is dedicated to advancing smart farming innovations that promote agricultural sustainability, productivity, and climate resilience through the convergence of automation, data analytics, and environmental intelligence. Profiles: Google Scholar | Orcid
Dr. Samsuzzaman is an emerging researcher in the field of smart and precision agriculture, focusing on the integration of artificial intelligence, sensor fusion, and digital technologies for sustainable crop and livestock production. His research interests span AI-based smart farming, controlled environment agriculture (CEA), vertical farming, remote sensing in agriculture, and digital livestock farming. His scientific contributions include several high-impact publications in international journals such as Agronomy, Horticulturae, Precision Agriculture, and the Journal of Animal Science and Technology, addressing topics like stress detection in seedlings, plant factory automation, and computer vision applications in livestock systems. His work demonstrates innovative use of machine learning, deep learning, and image processing to enhance environmental monitoring and resource efficiency in agricultural systems. With 181 citations, an h-index of 7, and an i10-index of 4, his research continues to gain recognition globally. Recognized with multiple academic honors, including Best Oral and Poster Presentation Awards from leading Korean scientific societies, Dr. Samsuzzaman actively contributes to interdisciplinary research projects funded by the Ministry of Agriculture, Food and Rural Affairs, South Korea. His recent studies on sensor fusion and machine vision for real-time crop stress quantification are pioneering steps toward next-generation precision agriculture technologies. As a member of international professional societies such as ASABE, ISPA, KSAM, and BKS, he is dedicated to advancing smart farming innovations that promote agricultural sustainability, productivity, and climate resilience through the convergence of automation, data analytics, and environmental intelligence. Profiles: Google Scholar | Orcid
Dr. Samsuzzaman is an emerging researcher in the field of smart and precision agriculture, focusing on the integration of artificial intelligence, sensor fusion, and digital technologies for sustainable crop and livestock production. His research interests span AI-based smart farming, controlled environment agriculture (CEA), vertical farming, remote sensing in agriculture, and digital livestock farming. His scientific contributions include several high-impact publications in international journals such as Agronomy, Horticulturae, Precision Agriculture, and the Journal of Animal Science and Technology, addressing topics like stress detection in seedlings, plant factory automation, and computer vision applications in livestock systems. His work demonstrates innovative use of machine learning, deep learning, and image processing to enhance environmental monitoring and resource efficiency in agricultural systems. With 181 citations, an h-index of 7, and an i10-index of 4, his research continues to gain recognition globally. Recognized with multiple academic honors, including Best Oral and Poster Presentation Awards from leading Korean scientific societies, Dr. Samsuzzaman actively contributes to interdisciplinary research projects funded by the Ministry of Agriculture, Food and Rural Affairs, South Korea. His recent studies on sensor fusion and machine vision for real-time crop stress quantification are pioneering steps toward next-generation precision agriculture technologies. As a member of international professional societies such as ASABE, ISPA, KSAM, and BKS, he is dedicated to advancing smart farming innovations that promote agricultural sustainability, productivity, and climate resilience through the convergence of automation, data analytics, and environmental intelligence. Profiles: Google Scholar | Orcid
Dr. Samsuzzaman is an emerging researcher in the field of smart and precision agriculture, focusing on the integration of artificial intelligence, sensor fusion, and digital technologies for sustainable crop and livestock production. His research interests span AI-based smart farming, controlled environment agriculture (CEA), vertical farming, remote sensing in agriculture, and digital livestock farming. His scientific contributions include several high-impact publications in international journals such as Agronomy, Horticulturae, Precision Agriculture, and the Journal of Animal Science and Technology, addressing topics like stress detection in seedlings, plant factory automation, and computer vision applications in livestock systems. His work demonstrates innovative use of machine learning, deep learning, and image processing to enhance environmental monitoring and resource efficiency in agricultural systems. With 181 citations, an h-index of 7, and an i10-index of 4, his research continues to gain recognition globally. Recognized with multiple academic honors, including Best Oral and Poster Presentation Awards from leading Korean scientific societies, Dr. Samsuzzaman actively contributes to interdisciplinary research projects funded by the Ministry of Agriculture, Food and Rural Affairs, South Korea. His recent studies on sensor fusion and machine vision for real-time crop stress quantification are pioneering steps toward next-generation precision agriculture technologies. As a member of international professional societies such as ASABE, ISPA, KSAM, and BKS, he is dedicated to advancing smart farming innovations that promote agricultural sustainability, productivity, and climate resilience through the convergence of automation, data analytics, and environmental intelligence. Profiles: Google Scholar | Orcid
Dr. Samsuzzaman is an emerging researcher in the field of smart and precision agriculture, focusing on the integration of artificial intelligence, sensor fusion, and digital technologies for sustainable crop and livestock production. His research interests span AI-based smart farming, controlled environment agriculture (CEA), vertical farming, remote sensing in agriculture, and digital livestock farming. His scientific contributions include several high-impact publications in international journals such as Agronomy, Horticulturae, Precision Agriculture, and the Journal of Animal Science and Technology, addressing topics like stress detection in seedlings, plant factory automation, and computer vision applications in livestock systems. His work demonstrates innovative use of machine learning, deep learning, and image processing to enhance environmental monitoring and resource efficiency in agricultural systems. With 181 citations, an h-index of 7, and an i10-index of 4, his research continues to gain recognition globally. Recognized with multiple academic honors, including Best Oral and Poster Presentation Awards from leading Korean scientific societies, Dr. Samsuzzaman actively contributes to interdisciplinary research projects funded by the Ministry of Agriculture, Food and Rural Affairs, South Korea. His recent studies on sensor fusion and machine vision for real-time crop stress quantification are pioneering steps toward next-generation precision agriculture technologies. As a member of international professional societies such as ASABE, ISPA, KSAM, and BKS, he is dedicated to advancing smart farming innovations that promote agricultural sustainability, productivity, and climate resilience through the convergence of automation, data analytics, and environmental intelligence. Profiles: Google Scholar | Orcid
Dr. Samsuzzaman is an emerging researcher in the field of smart and precision agriculture, focusing on the integration of artificial intelligence, sensor fusion, and digital technologies for sustainable crop and livestock production. His research interests span AI-based smart farming, controlled environment agriculture (CEA), vertical farming, remote sensing in agriculture, and digital livestock farming. His scientific contributions include several high-impact publications in international journals such as Agronomy, Horticulturae, Precision Agriculture, and the Journal of Animal Science and Technology, addressing topics like stress detection in seedlings, plant factory automation, and computer vision applications in livestock systems. His work demonstrates innovative use of machine learning, deep learning, and image processing to enhance environmental monitoring and resource efficiency in agricultural systems. With 181 citations, an h-index of 7, and an i10-index of 4, his research continues to gain recognition globally. Recognized with multiple academic honors, including Best Oral and Poster Presentation Awards from leading Korean scientific societies, Dr. Samsuzzaman actively contributes to interdisciplinary research projects funded by the Ministry of Agriculture, Food and Rural Affairs, South Korea. His recent studies on sensor fusion and machine vision for real-time crop stress quantification are pioneering steps toward next-generation precision agriculture technologies. As a member of international professional societies such as ASABE, ISPA, KSAM, and BKS, he is dedicated to advancing smart farming innovations that promote agricultural sustainability, productivity, and climate resilience through the convergence of automation, data analytics, and environmental intelligence. Profiles: Google Scholar | Orcid
Dr. Samsuzzaman is an emerging researcher in the field of smart and precision agriculture, focusing on the integration of artificial intelligence, sensor fusion, and digital technologies for sustainable crop and livestock production. His research interests span AI-based smart farming, controlled environment agriculture (CEA), vertical farming, remote sensing in agriculture, and digital livestock farming. His scientific contributions include several high-impact publications in international journals such as Agronomy, Horticulturae, Precision Agriculture, and the Journal of Animal Science and Technology, addressing topics like stress detection in seedlings, plant factory automation, and computer vision applications in livestock systems. His work demonstrates innovative use of machine learning, deep learning, and image processing to enhance environmental monitoring and resource efficiency in agricultural systems. With 181 citations, an h-index of 7, and an i10-index of 4, his research continues to gain recognition globally. Recognized with multiple academic honors, including Best Oral and Poster Presentation Awards from leading Korean scientific societies, Dr. Samsuzzaman actively contributes to interdisciplinary research projects funded by the Ministry of Agriculture, Food and Rural Affairs, South Korea. His recent studies on sensor fusion and machine vision for real-time crop stress quantification are pioneering steps toward next-generation precision agriculture technologies. As a member of international professional societies such as ASABE, ISPA, KSAM, and BKS, he is dedicated to advancing smart farming innovations that promote agricultural sustainability, productivity, and climate resilience through the convergence of automation, data analytics, and environmental intelligence. Profiles: Google Scholar | Orcid
Dr. Samsuzzaman is an emerging researcher in the field of smart and precision agriculture, focusing on the integration of artificial intelligence, sensor fusion, and digital technologies for sustainable crop and livestock production. His research interests span AI-based smart farming, controlled environment agriculture (CEA), vertical farming, remote sensing in agriculture, and digital livestock farming. His scientific contributions include several high-impact publications in international journals such as Agronomy, Horticulturae, Precision Agriculture, and the Journal of Animal Science and Technology, addressing topics like stress detection in seedlings, plant factory automation, and computer vision applications in livestock systems. His work demonstrates innovative use of machine learning, deep learning, and image processing to enhance environmental monitoring and resource efficiency in agricultural systems. With 181 citations, an h-index of 7, and an i10-index of 4, his research continues to gain recognition globally. Recognized with multiple academic honors, including Best Oral and Poster Presentation Awards from leading Korean scientific societies, Dr. Samsuzzaman actively contributes to interdisciplinary research projects funded by the Ministry of Agriculture, Food and Rural Affairs, South Korea. His recent studies on sensor fusion and machine vision for real-time crop stress quantification are pioneering steps toward next-generation precision agriculture technologies. As a member of international professional societies such as ASABE, ISPA, KSAM, and BKS, he is dedicated to advancing smart farming innovations that promote agricultural sustainability, productivity, and climate resilience through the convergence of automation, data analytics, and environmental intelligence. Profiles: Google Scholar | Orcid
Dr. Samsuzzaman is an emerging researcher in the field of smart and precision agriculture, focusing on the integration of artificial intelligence, sensor fusion, and digital technologies for sustainable crop and livestock production. His research interests span AI-based smart farming, controlled environment agriculture (CEA), vertical farming, remote sensing in agriculture, and digital livestock farming. His scientific contributions include several high-impact publications in international journals such as Agronomy, Horticulturae, Precision Agriculture, and the Journal of Animal Science and Technology, addressing topics like stress detection in seedlings, plant factory automation, and computer vision applications in livestock systems. His work demonstrates innovative use of machine learning, deep learning, and image processing to enhance environmental monitoring and resource efficiency in agricultural systems. With 181 citations, an h-index of 7, and an i10-index of 4, his research continues to gain recognition globally. Recognized with multiple academic honors, including Best Oral and Poster Presentation Awards from leading Korean scientific societies, Dr. Samsuzzaman actively contributes to interdisciplinary research projects funded by the Ministry of Agriculture, Food and Rural Affairs, South Korea. His recent studies on sensor fusion and machine vision for real-time crop stress quantification are pioneering steps toward next-generation precision agriculture technologies. As a member of international professional societies such as ASABE, ISPA, KSAM, and BKS, he is dedicated to advancing smart farming innovations that promote agricultural sustainability, productivity, and climate resilience through the convergence of automation, data analytics, and environmental intelligence. Profiles: Google Scholar | Orcid
Dr. Samsuzzaman is an emerging researcher in the field of smart and precision agriculture, focusing on the integration of artificial intelligence, sensor fusion, and digital technologies for sustainable crop and livestock production. His research interests span AI-based smart farming, controlled environment agriculture (CEA), vertical farming, remote sensing in agriculture, and digital livestock farming. His scientific contributions include several high-impact publications in international journals such as Agronomy, Horticulturae, Precision Agriculture, and the Journal of Animal Science and Technology, addressing topics like stress detection in seedlings, plant factory automation, and computer vision applications in livestock systems. His work demonstrates innovative use of machine learning, deep learning, and image processing to enhance environmental monitoring and resource efficiency in agricultural systems. With 181 citations, an h-index of 7, and an i10-index of 4, his research continues to gain recognition globally. Recognized with multiple academic honors, including Best Oral and Poster Presentation Awards from leading Korean scientific societies, Dr. Samsuzzaman actively contributes to interdisciplinary research projects funded by the Ministry of Agriculture, Food and Rural Affairs, South Korea. His recent studies on sensor fusion and machine vision for real-time crop stress quantification are pioneering steps toward next-generation precision agriculture technologies. As a member of international professional societies such as ASABE, ISPA, KSAM, and BKS, he is dedicated to advancing smart farming innovations that promote agricultural sustainability, productivity, and climate resilience through the convergence of automation, data analytics, and environmental intelligence. Profiles: Google Scholar | Orcid
Featured Publications Kabir, M. S. N., Reza, M. N., Chowdhury, M., Ali, M., Samsuzzaman, Ali, M. R., & others. (2023). Technological trends and engineering issues on vertical farms: A review. Horticulturae, 9(11), 1229. Reza, M. N., Ali, M. R., Kabir, M. S. N., Karim, M. R., Ahmed, S., Kyoung, H., Kim, G., & Samsuzzaman. (2024). Thermal imaging and computer vision technologies for the enhancement of pig husbandry: A review. Journal of Animal Science and Technology, 66(1), 31. Islam, S., Reza, M. N., Samsuzzaman, S. A., Cho, Y. J., Noh, D. H., Chung, S. O., & others. (2024). Machine vision and artificial intelligence for plant growth stress detection and monitoring: A review. Precision Agriculture, 6(1), 34. Islam, S., Samsuzzaman, Reza, M. N., Lee, K. H., Ahmed, S., Cho, Y. J., Noh, D. H., & others. (2024). Image processing and support vector machine (SVM) for classifying environmental stress symptoms of pepper seedlings grown in a plant factory. Agronomy, 14(9), 2043. Islam, S., Reza, M. N., Ahmed, S., Samsuzzaman, Lee, K. H., Cho, Y. J., Noh, D. H., & others. (2024). Nutrient stress symptom detection in cucumber seedlings using segmented regression and a mask region-based convolutional neural network model. Agriculture, 14(8), 1390.*
Featured Publications Kabir, M. S. N., Reza, M. N., Chowdhury, M., Ali, M., Samsuzzaman, Ali, M. R., & others. (2023). Technological trends and engineering issues on vertical farms: A review. Horticulturae, 9(11), 1229. Reza, M. N., Ali, M. R., Kabir, M. S. N., Karim, M. R., Ahmed, S., Kyoung, H., Kim, G., & Samsuzzaman. (2024). Thermal imaging and computer vision technologies for the enhancement of pig husbandry: A review. Journal of Animal Science and Technology, 66(1), 31. Islam, S., Reza, M. N., Samsuzzaman, S. A., Cho, Y. J., Noh, D. H., Chung, S. O., & others. (2024). Machine vision and artificial intelligence for plant growth stress detection and monitoring: A review. Precision Agriculture, 6(1), 34. Islam, S., Samsuzzaman, Reza, M. N., Lee, K. H., Ahmed, S., Cho, Y. J., Noh, D. H., & others. (2024). Image processing and support vector machine (SVM) for classifying environmental stress symptoms of pepper seedlings grown in a plant factory. Agronomy, 14(9), 2043. Islam, S., Reza, M. N., Ahmed, S., Samsuzzaman, Lee, K. H., Cho, Y. J., Noh, D. H., & others. (2024). Nutrient stress symptom detection in cucumber seedlings using segmented regression and a mask region-based convolutional neural network model. Agriculture, 14(8), 1390.*
Featured Publications Kabir, M. S. N., Reza, M. N., Chowdhury, M., Ali, M., Samsuzzaman, Ali, M. R., & others. (2023). Technological trends and engineering issues on vertical farms: A review. Horticulturae, 9(11), 1229. Reza, M. N., Ali, M. R., Kabir, M. S. N., Karim, M. R., Ahmed, S., Kyoung, H., Kim, G., & Samsuzzaman. (2024). Thermal imaging and computer vision technologies for the enhancement of pig husbandry: A review. Journal of Animal Science and Technology, 66(1), 31. Islam, S., Reza, M. N., Samsuzzaman, S. A., Cho, Y. J., Noh, D. H., Chung, S. O., & others. (2024). Machine vision and artificial intelligence for plant growth stress detection and monitoring: A review. Precision Agriculture, 6(1), 34. Islam, S., Samsuzzaman, Reza, M. N., Lee, K. H., Ahmed, S., Cho, Y. J., Noh, D. H., & others. (2024). Image processing and support vector machine (SVM) for classifying environmental stress symptoms of pepper seedlings grown in a plant factory. Agronomy, 14(9), 2043. Islam, S., Reza, M. N., Ahmed, S., Samsuzzaman, Lee, K. H., Cho, Y. J., Noh, D. H., & others. (2024). Nutrient stress symptom detection in cucumber seedlings using segmented regression and a mask region-based convolutional neural network model. Agriculture, 14(8), 1390.*
Featured Publications Kabir, M. S. N., Reza, M. N., Chowdhury, M., Ali, M., Samsuzzaman, Ali, M. R., & others. (2023). Technological trends and engineering issues on vertical farms: A review. Horticulturae, 9(11), 1229. Reza, M. N., Ali, M. R., Kabir, M. S. N., Karim, M. R., Ahmed, S., Kyoung, H., Kim, G., & Samsuzzaman. (2024). Thermal imaging and computer vision technologies for the enhancement of pig husbandry: A review. Journal of Animal Science and Technology, 66(1), 31. Islam, S., Reza, M. N., Samsuzzaman, S. A., Cho, Y. J., Noh, D. H., Chung, S. O., & others. (2024). Machine vision and artificial intelligence for plant growth stress detection and monitoring: A review. Precision Agriculture, 6(1), 34. Islam, S., Samsuzzaman, Reza, M. N., Lee, K. H., Ahmed, S., Cho, Y. J., Noh, D. H., & others. (2024). Image processing and support vector machine (SVM) for classifying environmental stress symptoms of pepper seedlings grown in a plant factory. Agronomy, 14(9), 2043. Islam, S., Reza, M. N., Ahmed, S., Samsuzzaman, Lee, K. H., Cho, Y. J., Noh, D. H., & others. (2024). Nutrient stress symptom detection in cucumber seedlings using segmented regression and a mask region-based convolutional neural network model. Agriculture, 14(8), 1390.*
Featured Publications Kabir, M. S. N., Reza, M. N., Chowdhury, M., Ali, M., Samsuzzaman, Ali, M. R., & others. (2023). Technological trends and engineering issues on vertical farms: A review. Horticulturae, 9(11), 1229. Reza, M. N., Ali, M. R., Kabir, M. S. N., Karim, M. R., Ahmed, S., Kyoung, H., Kim, G., & Samsuzzaman. (2024). Thermal imaging and computer vision technologies for the enhancement of pig husbandry: A review. Journal of Animal Science and Technology, 66(1), 31. Islam, S., Reza, M. N., Samsuzzaman, S. A., Cho, Y. J., Noh, D. H., Chung, S. O., & others. (2024). Machine vision and artificial intelligence for plant growth stress detection and monitoring: A review. Precision Agriculture, 6(1), 34. Islam, S., Samsuzzaman, Reza, M. N., Lee, K. H., Ahmed, S., Cho, Y. J., Noh, D. H., & others. (2024). Image processing and support vector machine (SVM) for classifying environmental stress symptoms of pepper seedlings grown in a plant factory. Agronomy, 14(9), 2043. Islam, S., Reza, M. N., Ahmed, S., Samsuzzaman, Lee, K. H., Cho, Y. J., Noh, D. H., & others. (2024). Nutrient stress symptom detection in cucumber seedlings using segmented regression and a mask region-based convolutional neural network model. Agriculture, 14(8), 1390.*
Featured Publications Kabir, M. S. N., Reza, M. N., Chowdhury, M., Ali, M., Samsuzzaman, Ali, M. R., & others. (2023). Technological trends and engineering issues on vertical farms: A review. Horticulturae, 9(11), 1229. Reza, M. N., Ali, M. R., Kabir, M. S. N., Karim, M. R., Ahmed, S., Kyoung, H., Kim, G., & Samsuzzaman. (2024). Thermal imaging and computer vision technologies for the enhancement of pig husbandry: A review. Journal of Animal Science and Technology, 66(1), 31. Islam, S., Reza, M. N., Samsuzzaman, S. A., Cho, Y. J., Noh, D. H., Chung, S. O., & others. (2024). Machine vision and artificial intelligence for plant growth stress detection and monitoring: A review. Precision Agriculture, 6(1), 34. Islam, S., Samsuzzaman, Reza, M. N., Lee, K. H., Ahmed, S., Cho, Y. J., Noh, D. H., & others. (2024). Image processing and support vector machine (SVM) for classifying environmental stress symptoms of pepper seedlings grown in a plant factory. Agronomy, 14(9), 2043. Islam, S., Reza, M. N., Ahmed, S., Samsuzzaman, Lee, K. H., Cho, Y. J., Noh, D. H., & others. (2024). Nutrient stress symptom detection in cucumber seedlings using segmented regression and a mask region-based convolutional neural network model. Agriculture, 14(8), 1390.*
Featured Publications Kabir, M. S. N., Reza, M. N., Chowdhury, M., Ali, M., Samsuzzaman, Ali, M. R., & others. (2023). Technological trends and engineering issues on vertical farms: A review. Horticulturae, 9(11), 1229. Reza, M. N., Ali, M. R., Kabir, M. S. N., Karim, M. R., Ahmed, S., Kyoung, H., Kim, G., & Samsuzzaman. (2024). Thermal imaging and computer vision technologies for the enhancement of pig husbandry: A review. Journal of Animal Science and Technology, 66(1), 31. Islam, S., Reza, M. N., Samsuzzaman, S. A., Cho, Y. J., Noh, D. H., Chung, S. O., & others. (2024). Machine vision and artificial intelligence for plant growth stress detection and monitoring: A review. Precision Agriculture, 6(1), 34. Islam, S., Samsuzzaman, Reza, M. N., Lee, K. H., Ahmed, S., Cho, Y. J., Noh, D. H., & others. (2024). Image processing and support vector machine (SVM) for classifying environmental stress symptoms of pepper seedlings grown in a plant factory. Agronomy, 14(9), 2043. Islam, S., Reza, M. N., Ahmed, S., Samsuzzaman, Lee, K. H., Cho, Y. J., Noh, D. H., & others. (2024). Nutrient stress symptom detection in cucumber seedlings using segmented regression and a mask region-based convolutional neural network model. Agriculture, 14(8), 1390.*
Featured Publications Kabir, M. S. N., Reza, M. N., Chowdhury, M., Ali, M., Samsuzzaman, Ali, M. R., & others. (2023). Technological trends and engineering issues on vertical farms: A review. Horticulturae, 9(11), 1229. Reza, M. N., Ali, M. R., Kabir, M. S. N., Karim, M. R., Ahmed, S., Kyoung, H., Kim, G., & Samsuzzaman. (2024). Thermal imaging and computer vision technologies for the enhancement of pig husbandry: A review. Journal of Animal Science and Technology, 66(1), 31. Islam, S., Reza, M. N., Samsuzzaman, S. A., Cho, Y. J., Noh, D. H., Chung, S. O., & others. (2024). Machine vision and artificial intelligence for plant growth stress detection and monitoring: A review. Precision Agriculture, 6(1), 34. Islam, S., Samsuzzaman, Reza, M. N., Lee, K. H., Ahmed, S., Cho, Y. J., Noh, D. H., & others. (2024). Image processing and support vector machine (SVM) for classifying environmental stress symptoms of pepper seedlings grown in a plant factory. Agronomy, 14(9), 2043. Islam, S., Reza, M. N., Ahmed, S., Samsuzzaman, Lee, K. H., Cho, Y. J., Noh, D. H., & others. (2024). Nutrient stress symptom detection in cucumber seedlings using segmented regression and a mask region-based convolutional neural network model. Agriculture, 14(8), 1390.*
Featured Publications Kabir, M. S. N., Reza, M. N., Chowdhury, M., Ali, M., Samsuzzaman, Ali, M. R., & others. (2023). Technological trends and engineering issues on vertical farms: A review. Horticulturae, 9(11), 1229. Reza, M. N., Ali, M. R., Kabir, M. S. N., Karim, M. R., Ahmed, S., Kyoung, H., Kim, G., & Samsuzzaman. (2024). Thermal imaging and computer vision technologies for the enhancement of pig husbandry: A review. Journal of Animal Science and Technology, 66(1), 31. Islam, S., Reza, M. N., Samsuzzaman, S. A., Cho, Y. J., Noh, D. H., Chung, S. O., & others. (2024). Machine vision and artificial intelligence for plant growth stress detection and monitoring: A review. Precision Agriculture, 6(1), 34. Islam, S., Samsuzzaman, Reza, M. N., Lee, K. H., Ahmed, S., Cho, Y. J., Noh, D. H., & others. (2024). Image processing and support vector machine (SVM) for classifying environmental stress symptoms of pepper seedlings grown in a plant factory. Agronomy, 14(9), 2043. Islam, S., Reza, M. N., Ahmed, S., Samsuzzaman, Lee, K. H., Cho, Y. J., Noh, D. H., & others. (2024). Nutrient stress symptom detection in cucumber seedlings using segmented regression and a mask region-based convolutional neural network model. Agriculture, 14(8), 1390.*
Featured Publications Kabir, M. S. N., Reza, M. N., Chowdhury, M., Ali, M., Samsuzzaman, Ali, M. R., & others. (2023). Technological trends and engineering issues on vertical farms: A review. Horticulturae, 9(11), 1229. Reza, M. N., Ali, M. R., Kabir, M. S. N., Karim, M. R., Ahmed, S., Kyoung, H., Kim, G., & Samsuzzaman. (2024). Thermal imaging and computer vision technologies for the enhancement of pig husbandry: A review. Journal of Animal Science and Technology, 66(1), 31. Islam, S., Reza, M. N., Samsuzzaman, S. A., Cho, Y. J., Noh, D. H., Chung, S. O., & others. (2024). Machine vision and artificial intelligence for plant growth stress detection and monitoring: A review. Precision Agriculture, 6(1), 34. Islam, S., Samsuzzaman, Reza, M. N., Lee, K. H., Ahmed, S., Cho, Y. J., Noh, D. H., & others. (2024). Image processing and support vector machine (SVM) for classifying environmental stress symptoms of pepper seedlings grown in a plant factory. Agronomy, 14(9), 2043. Islam, S., Reza, M. N., Ahmed, S., Samsuzzaman, Lee, K. H., Cho, Y. J., Noh, D. H., & others. (2024). Nutrient stress symptom detection in cucumber seedlings using segmented regression and a mask region-based convolutional neural network model. Agriculture, 14(8), 1390.*
Featured Publications Kabir, M. S. N., Reza, M. N., Chowdhury, M., Ali, M., Samsuzzaman, Ali, M. R., & others. (2023). Technological trends and engineering issues on vertical farms: A review. Horticulturae, 9(11), 1229. Reza, M. N., Ali, M. R., Kabir, M. S. N., Karim, M. R., Ahmed, S., Kyoung, H., Kim, G., & Samsuzzaman. (2024). Thermal imaging and computer vision technologies for the enhancement of pig husbandry: A review. Journal of Animal Science and Technology, 66(1), 31. Islam, S., Reza, M. N., Samsuzzaman, S. A., Cho, Y. J., Noh, D. H., Chung, S. O., & others. (2024). Machine vision and artificial intelligence for plant growth stress detection and monitoring: A review. Precision Agriculture, 6(1), 34. Islam, S., Samsuzzaman, Reza, M. N., Lee, K. H., Ahmed, S., Cho, Y. J., Noh, D. H., & others. (2024). Image processing and support vector machine (SVM) for classifying environmental stress symptoms of pepper seedlings grown in a plant factory. Agronomy, 14(9), 2043. Islam, S., Reza, M. N., Ahmed, S., Samsuzzaman, Lee, K. H., Cho, Y. J., Noh, D. H., & others. (2024). Nutrient stress symptom detection in cucumber seedlings using segmented regression and a mask region-based convolutional neural network model. Agriculture, 14(8), 1390.*
Kabir, M. S. N., Reza, M. N., Chowdhury, M., Ali, M., Samsuzzaman, Ali, M. R., & others. (2023). Technological trends and engineering issues on vertical farms: A review. Horticulturae, 9(11), 1229. Reza, M. N., Ali, M. R., Kabir, M. S. N., Karim, M. R., Ahmed, S., Kyoung, H., Kim, G., & Samsuzzaman. (2024). Thermal imaging and computer vision technologies for the enhancement of pig husbandry: A review. Journal of Animal Science and Technology, 66(1), 31. Islam, S., Reza, M. N., Samsuzzaman, S. A., Cho, Y. J., Noh, D. H., Chung, S. O., & others. (2024). Machine vision and artificial intelligence for plant growth stress detection and monitoring: A review. Precision Agriculture, 6(1), 34. Islam, S., Samsuzzaman, Reza, M. N., Lee, K. H., Ahmed, S., Cho, Y. J., Noh, D. H., & others. (2024). Image processing and support vector machine (SVM) for classifying environmental stress symptoms of pepper seedlings grown in a plant factory. Agronomy, 14(9), 2043. Islam, S., Reza, M. N., Ahmed, S., Samsuzzaman, Lee, K. H., Cho, Y. J., Noh, D. H., & others. (2024). Nutrient stress symptom detection in cucumber seedlings using segmented regression and a mask region-based convolutional neural network model. Agriculture, 14(8), 1390.*
Kabir, M. S. N., Reza, M. N., Chowdhury, M., Ali, M., Samsuzzaman, Ali, M. R., & others. (2023). Technological trends and engineering issues on vertical farms: A review. Horticulturae, 9(11), 1229. Reza, M. N., Ali, M. R., Kabir, M. S. N., Karim, M. R., Ahmed, S., Kyoung, H., Kim, G., & Samsuzzaman. (2024). Thermal imaging and computer vision technologies for the enhancement of pig husbandry: A review. Journal of Animal Science and Technology, 66(1), 31. Islam, S., Reza, M. N., Samsuzzaman, S. A., Cho, Y. J., Noh, D. H., Chung, S. O., & others. (2024). Machine vision and artificial intelligence for plant growth stress detection and monitoring: A review. Precision Agriculture, 6(1), 34. Islam, S., Samsuzzaman, Reza, M. N., Lee, K. H., Ahmed, S., Cho, Y. J., Noh, D. H., & others. (2024). Image processing and support vector machine (SVM) for classifying environmental stress symptoms of pepper seedlings grown in a plant factory. Agronomy, 14(9), 2043. Islam, S., Reza, M. N., Ahmed, S., Samsuzzaman, Lee, K. H., Cho, Y. J., Noh, D. H., & others. (2024). Nutrient stress symptom detection in cucumber seedlings using segmented regression and a mask region-based convolutional neural network model. Agriculture, 14(8), 1390.*
Kabir, M. S. N., Reza, M. N., Chowdhury, M., Ali, M., Samsuzzaman, Ali, M. R., & others. (2023). Technological trends and engineering issues on vertical farms: A review. Horticulturae, 9(11), 1229.