Dr. Weindorf’s research interests are at the nexus of soil science and environmental quality assessment. He is an internationally recognized authority in proximal sensor characterization of soils with extensive work in hydrocarbon and heavy metal polluted soils. Over a 20+ year career, he has worked in 30 countries worldwide and serves on two Elsevier editorial boards (Pedosphere and Geoderma).He has published >200 peer reviewed research papers with >6000 citations of his work. He is Fulbright Scholar, a Fulbright Specialist, and Fellow & Presidential Award winner in the Soil Science Society of America. He has offered invited testimony before the US Congress and regularly provides expert testimony in legal matters germane to environmental quality assessment.Dr. Chakraborty's research interests lie in proximal soil sensors in combination with data mining and machine learning, with a focus on developing scalable algorithms for rapidly and non-invasively predicting soil properties. In particular, he has worked on developing methods for sensors like portable XRF, diffuse reflectance spectroscopy, smartphone-based soil sensing, Nix color sensor, digital soil mapping, as well as techniques for dealing with real-time soil characterization. He has published over 100 research articles in various international journals and conferences, and his work has been cited over 3,700 times. He has received several awards and honors for his contributions to the field of soil science, including the Australia awards fellowship and SPESS garden scholarship (USA). In addition to his research, Chakraborty is also actively involved in teaching and mentoring students at IIT Kharagpur. He has supervised several Ph.D. and M. Tech. students and has also been involved in the development of several online courses on soil science. Today, Dr. Li’s research is focused on statistics and machine learning. He has published >75 peer reviewed research papers with >1,300 citations of his work.

• 1. Introduction2. Fundamentals of Soil Characterization3. Sensor-based Soil Characterization Techniques4. Machine Learning and AI Techniques5. Applications of Machine Learning and AI in Soil Science6. Image Acquisition and Processing Techniques7. Applications of Machine Learning and AI in Image-Based Soil characterization8. Applications of Sensor + AI based Soil Characterization9. Case Studies10. Conclusion and Future Directions

Dr. Weindorf’s research interests are at the nexus of soil science and environmental quality assessment. He is an internationally recognized authority in proximal sensor characterization of soils with extensive work in hydrocarbon and heavy metal polluted soils. Over a 20+ year career, he has worked in 30 countries worldwide and serves on two Elsevier editorial boards (Pedosphere and Geoderma).He has published >200 peer reviewed research papers with >6000 citations of his work. He is Fulbright Scholar, a Fulbright Specialist, and Fellow & Presidential Award winner in the Soil Science Society of America. He has offered invited testimony before the US Congress and regularly provides expert testimony in legal matters germane to environmental quality assessment.Dr. Chakraborty's research interests lie in proximal soil sensors in combination with data mining and machine learning, with a focus on developing scalable algorithms for rapidly and non-invasively predicting soil properties. In particular, he has worked on developing methods for sensors like portable XRF, diffuse reflectance spectroscopy, smartphone-based soil sensing, Nix color sensor, digital soil mapping, as well as techniques for dealing with real-time soil characterization. He has published over 100 research articles in various international journals and conferences, and his work has been cited over 3,700 times. He has received several awards and honors for his contributions to the field of soil science, including the Australia awards fellowship and SPESS garden scholarship (USA). In addition to his research, Chakraborty is also actively involved in teaching and mentoring students at IIT Kharagpur. He has supervised several Ph.D. and M. Tech. students and has also been involved in the development of several online courses on soil science. Today, Dr. Li’s research is focused on statistics and machine learning. He has published >75 peer reviewed research papers with >1,300 citations of his work.

• 1. Introduction2. Fundamentals of Soil Characterization3. Sensor-based Soil Characterization Techniques4. Machine Learning and AI Techniques5. Applications of Machine Learning and AI in Soil Science6. Image Acquisition and Processing Techniques7. Applications of Machine Learning and AI in Image-Based Soil characterization8. Applications of Sensor + AI based Soil Characterization9. Case Studies10. Conclusion and Future Directions

Dr. Weindorf’s research interests are at the nexus of soil science and environmental quality assessment. He is an internationally recognized authority in proximal sensor characterization of soils with extensive work in hydrocarbon and heavy metal polluted soils. Over a 20+ year career, he has worked in 30 countries worldwide and serves on two Elsevier editorial boards (Pedosphere and Geoderma).He has published >200 peer reviewed research papers with >6000 citations of his work. He is Fulbright Scholar, a Fulbright Specialist, and Fellow & Presidential Award winner in the Soil Science Society of America. He has offered invited testimony before the US Congress and regularly provides expert testimony in legal matters germane to environmental quality assessment.Dr. Chakraborty's research interests lie in proximal soil sensors in combination with data mining and machine learning, with a focus on developing scalable algorithms for rapidly and non-invasively predicting soil properties. In particular, he has worked on developing methods for sensors like portable XRF, diffuse reflectance spectroscopy, smartphone-based soil sensing, Nix color sensor, digital soil mapping, as well as techniques for dealing with real-time soil characterization. He has published over 100 research articles in various international journals and conferences, and his work has been cited over 3,700 times. He has received several awards and honors for his contributions to the field of soil science, including the Australia awards fellowship and SPESS garden scholarship (USA). In addition to his research, Chakraborty is also actively involved in teaching and mentoring students at IIT Kharagpur. He has supervised several Ph.D. and M. Tech. students and has also been involved in the development of several online courses on soil science. Today, Dr. Li’s research is focused on statistics and machine learning. He has published >75 peer reviewed research papers with >1,300 citations of his work.

• 1. Introduction2. Fundamentals of Soil Characterization3. Sensor-based Soil Characterization Techniques4. Machine Learning and AI Techniques5. Applications of Machine Learning and AI in Soil Science6. Image Acquisition and Processing Techniques7. Applications of Machine Learning and AI in Image-Based Soil characterization8. Applications of Sensor + AI based Soil Characterization9. Case Studies10. Conclusion and Future Directions