Skoltech Digital Agro
Publications
Skoltech, Environmental Data Science and Engineering Lab
2021
2021
Gasanov, M., Merkulov, D., Nikitin, A., Matveev, S., Stasenko, N., Petrovskaia, A., Pukalchik, M., Oseledets, I., 2021. A New Multi-objective Approach to Optimize Irrigation Using a Crop Simulation Model and Weather History, in: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (Eds.), Computational Science – ICCS 2021, Lecture Notes in Computer Science. Springer International Publishing, Cham, pp. 75–88. https://doi.org/10.1007/978-3-030-77970-2_7
Illarionova, S., Nesteruk, S., Shadrin, D., Ignatiev, V., Pukalchik, M., Oseledets, I., 2021. MixChannel: Advanced Augmentation for Multispectral Satellite Images. Remote Sensing 13, 2181. https://doi.org/10.3390/rs13112181
Nesteruk, S., Shadrin, D., Pukalchik, M., Somov, A., Zeidler, C., Zabel, P., Schubert, D., 2021. Image Compression and Plants Classification Using Machine Learning in Controlled-Environment Agriculture: Antarctic Station Use Case. IEEE Sensors J. 1–1. https://doi.org/10.1109/JSEN.2021.3050084
Shadrin, D., Nikitin, A., Tregubova, P., Terekhova, V., Jana, R., Matveev, S., Pukalchik, M., 2021. An Automated Approach to Groundwater Quality Monitoring—Geospatial Mapping Based on Combined Application of Gaussian Process Regression and Bayesian Information Criterion. Water 13, 400. https://doi.org/10.3390/w13040400
Stasenko, N., Chernova, E., Shadrin, D., Ovchinnikov, G., Krivolapov, I., Pukalchik, M., 2021. Deep Learning for improving the storage process: Accurate and automatic segmentation of spoiled areas on apples, in: 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). Presented at the 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), IEEE, Glasgow, United Kingdom, pp. 1–6. https://doi.org/10.1109/I2MTC50364.2021.9460071
Vypirailenko, D., Kiseleva, E., Shadrin, D., Pukalchik, M., 2021. Deep learning techniques for enhancement of weeds growth classification, in: 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). Presented at the 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), IEEE, Glasgow, United Kingdom, pp. 1–6. https://doi.org/10.1109/I2MTC50364.2021.9459976
Yudina, E., Petrovskaia, A., Shadrin, D., Tregubova, P., Chernova, E., Pukalchik, M., Oseledets, I., 2021. Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use Case. Water 13, 888. https://doi.org/10.3390/w13070888
Chaouachi, M., Marzouk, T., Jallouli, S., Elkahoui, S., Gentzbittel, L., Ben, C., & Djébali, N. (2021). Activity assessment of tomato endophytic bacteria bioactive compounds for the postharvest biocontrol of Botrytis cinerea. Postharvest Biology and Technology, 172, 111389.
Illarionova, S., Nesteruk, S., Shadrin, D., Ignatiev, V., Pukalchik, M., Oseledets, I., 2021. MixChannel: Advanced Augmentation for Multispectral Satellite Images. Remote Sensing 13, 2181. https://doi.org/10.3390/rs13112181
Nesteruk, S., Shadrin, D., Pukalchik, M., Somov, A., Zeidler, C., Zabel, P., Schubert, D., 2021. Image Compression and Plants Classification Using Machine Learning in Controlled-Environment Agriculture: Antarctic Station Use Case. IEEE Sensors J. 1–1. https://doi.org/10.1109/JSEN.2021.3050084
Shadrin, D., Nikitin, A., Tregubova, P., Terekhova, V., Jana, R., Matveev, S., Pukalchik, M., 2021. An Automated Approach to Groundwater Quality Monitoring—Geospatial Mapping Based on Combined Application of Gaussian Process Regression and Bayesian Information Criterion. Water 13, 400. https://doi.org/10.3390/w13040400
Stasenko, N., Chernova, E., Shadrin, D., Ovchinnikov, G., Krivolapov, I., Pukalchik, M., 2021. Deep Learning for improving the storage process: Accurate and automatic segmentation of spoiled areas on apples, in: 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). Presented at the 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), IEEE, Glasgow, United Kingdom, pp. 1–6. https://doi.org/10.1109/I2MTC50364.2021.9460071
Vypirailenko, D., Kiseleva, E., Shadrin, D., Pukalchik, M., 2021. Deep learning techniques for enhancement of weeds growth classification, in: 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). Presented at the 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), IEEE, Glasgow, United Kingdom, pp. 1–6. https://doi.org/10.1109/I2MTC50364.2021.9459976
Yudina, E., Petrovskaia, A., Shadrin, D., Tregubova, P., Chernova, E., Pukalchik, M., Oseledets, I., 2021. Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use Case. Water 13, 888. https://doi.org/10.3390/w13070888
Chaouachi, M., Marzouk, T., Jallouli, S., Elkahoui, S., Gentzbittel, L., Ben, C., & Djébali, N. (2021). Activity assessment of tomato endophytic bacteria bioactive compounds for the postharvest biocontrol of Botrytis cinerea. Postharvest Biology and Technology, 172, 111389.
2020
2020
Shadrin, D., Pukalchik, M., Kovaleva, E., & Fedorov, M. (2020). Artificial intelligence models to predict acute phytotoxicity in petroleum contaminated soils. Ecotoxicology and Environmental Safety, 194, 110410.
Gasanov, M., Petrovskaia, A., Nikitin, A., Matveev, S., Tregubova, P., Pukalchik, M., & Oseledets, I. Sensitivity analysis of soil parameters in crop model supported with high-throughput computing, ICCS, 2020
Shadrin, D., Pukalchik, M., Uryasheva, A., Rodichenko, N., & Tsetserukou, D. (2020). Hyper-spectral NIR and MIR data and optimal wavebands for detecting of apple trees diseases. arXiv preprint arXiv:2004.02325, ICLR 2020
Matvienko, I., Gasanov, M., Petrovskaia, A., Jana, R. B., Pukalchik, M., & Oseledets, I. (2020). Bayesian aggregation improves traditional single image crop classification approaches. arXiv preprint arXiv:2004.03468, ICLR 2020
Petrovskaia, A., Jana, R. B., & Oseledets, I. V. (2020). A single image deep learning approach to restoration of corrupted remote sensing products. arXiv preprint arXiv:2004.04209, ICLR 2020
Shashkova, T. I., Martynova, E. U., Ayupova, A. F., Shumskiy, A. A., Ogurtsova, P. A., Kostyunina, O. V., ... & Zinovieva, N. A. (2020). Development of a low-density panel for genomic selection of pigs in Russia. Translational animal science, 4(1), 264-274.
Kostyunina, O. V., Abdelmanova, A. S., Martynova, E. U., & Zinovieva, N. A. A SEARCH FOR GENOMIC REGIONS CARRYING THE LETHAL GENETIC VARIANTS IN THE DUROC PIGS. BIOLOGY AGRICULTURAL, 275.
Kanapin, A. A., Sokolkova, A. B., Samsonova, A. A., Shchegolkov, A. V., Boldyrev, S. V., Aupova, A. F., ... & Samsonova, M. G. (2020). Genetic Variants Associated with Productivity and Contents of Protein and Oil in Soybeans. Biophysics, 65(2), 241-249.
Gasanov, M., Petrovskaia, A., Nikitin, A., Matveev, S., Tregubova, P., Pukalchik, M., & Oseledets, I. Sensitivity analysis of soil parameters in crop model supported with high-throughput computing, ICCS, 2020
Shadrin, D., Pukalchik, M., Uryasheva, A., Rodichenko, N., & Tsetserukou, D. (2020). Hyper-spectral NIR and MIR data and optimal wavebands for detecting of apple trees diseases. arXiv preprint arXiv:2004.02325, ICLR 2020
Matvienko, I., Gasanov, M., Petrovskaia, A., Jana, R. B., Pukalchik, M., & Oseledets, I. (2020). Bayesian aggregation improves traditional single image crop classification approaches. arXiv preprint arXiv:2004.03468, ICLR 2020
Petrovskaia, A., Jana, R. B., & Oseledets, I. V. (2020). A single image deep learning approach to restoration of corrupted remote sensing products. arXiv preprint arXiv:2004.04209, ICLR 2020
Shashkova, T. I., Martynova, E. U., Ayupova, A. F., Shumskiy, A. A., Ogurtsova, P. A., Kostyunina, O. V., ... & Zinovieva, N. A. (2020). Development of a low-density panel for genomic selection of pigs in Russia. Translational animal science, 4(1), 264-274.
Kostyunina, O. V., Abdelmanova, A. S., Martynova, E. U., & Zinovieva, N. A. A SEARCH FOR GENOMIC REGIONS CARRYING THE LETHAL GENETIC VARIANTS IN THE DUROC PIGS. BIOLOGY AGRICULTURAL, 275.
Kanapin, A. A., Sokolkova, A. B., Samsonova, A. A., Shchegolkov, A. V., Boldyrev, S. V., Aupova, A. F., ... & Samsonova, M. G. (2020). Genetic Variants Associated with Productivity and Contents of Protein and Oil in Soybeans. Biophysics, 65(2), 241-249.
2019
2019
Nikitin, A., Fastovets, I., Shadrin, D., Pukalchik, M., & Oseledets, I. (2019). Bayesian optimization for seed germination. Plant methods, 15(1), 43.
Pukalchik, M., Kydralieva, K., Yakimenko, O., Fedoseeva, E., & Terekhova, V. (2019). Outlining the potential role of humic products in modifying biological properties of the soil—a review. Frontiers in Environmental Science, 7, 80.
Pukalchik, M. A., Katrutsa, A. M., Shadrin, D., Terekhova, V. A., & Oseledets, I. V. (2019). Machine learning methods for estimation the indicators of phosphogypsum influence in soil. Journal of soils and sediments, 19(5), 2265-2276.
Jana, R. B., & Petrovskaia, A. (2019). 3D representation of soil structure using Generative Adversarial Networks. AGUFM, 2019, H31I-1835.
Chernova, A., Mazin, P., Goryunova, S., Goryunov, D., Demurin, Y., Gorlova, L., ... & Khaitovich, P. (2019). Ultra-performance liquid chromatography-mass spectrometry for precise fatty acid profiling of oilseed crops. PeerJ, 7, e6547.
Goryunova, S. V., Goryunov, D. V., Chernova, A. I., Martynova, E. U., Dmitriev, A. E., Boldyrev, S. V., ... & Demurin, Y. N. (2019). Genetic and phenotypic diversity of the sunflower collection of the Pustovoit All-Russia Research Institute of Oil Crops (VNIIMK). Helia, 42(70), 45-60.
Chernova, A., Gubaev, R., Mazin, P., Goryunova, S., Demurin, Y., Gorlova, L., ... & Khaytovich, P. (2019). UPLC–MS Triglyceride Profiling in Sunflower and Rapeseed Seeds. Biomolecules, 9(1), 9.
Goryunov, D. V., Anisimova, I. N., Gavrilova, V. A., Chernova, A. I., Sotnikova, E. A., Martynova, E. U., ... & Goryunova, S. V. (2019). Association mapping of fertility restorer gene for CMS PET1 in sunflower. Agronomy, 9(2), 49.
Chernova, A. I., & Martynova, E. U. (2019). High-throughput technologies for sunflower oil improvement. Current Challenges in Plant Genetics, Genomics, Bioinformatics, and Biotechnology, 24, 227.
Pukalchik, M., Kydralieva, K., Yakimenko, O., Fedoseeva, E., & Terekhova, V. (2019). Outlining the potential role of humic products in modifying biological properties of the soil—a review. Frontiers in Environmental Science, 7, 80.
Pukalchik, M. A., Katrutsa, A. M., Shadrin, D., Terekhova, V. A., & Oseledets, I. V. (2019). Machine learning methods for estimation the indicators of phosphogypsum influence in soil. Journal of soils and sediments, 19(5), 2265-2276.
Jana, R. B., & Petrovskaia, A. (2019). 3D representation of soil structure using Generative Adversarial Networks. AGUFM, 2019, H31I-1835.
Chernova, A., Mazin, P., Goryunova, S., Goryunov, D., Demurin, Y., Gorlova, L., ... & Khaitovich, P. (2019). Ultra-performance liquid chromatography-mass spectrometry for precise fatty acid profiling of oilseed crops. PeerJ, 7, e6547.
Goryunova, S. V., Goryunov, D. V., Chernova, A. I., Martynova, E. U., Dmitriev, A. E., Boldyrev, S. V., ... & Demurin, Y. N. (2019). Genetic and phenotypic diversity of the sunflower collection of the Pustovoit All-Russia Research Institute of Oil Crops (VNIIMK). Helia, 42(70), 45-60.
Chernova, A., Gubaev, R., Mazin, P., Goryunova, S., Demurin, Y., Gorlova, L., ... & Khaytovich, P. (2019). UPLC–MS Triglyceride Profiling in Sunflower and Rapeseed Seeds. Biomolecules, 9(1), 9.
Goryunov, D. V., Anisimova, I. N., Gavrilova, V. A., Chernova, A. I., Sotnikova, E. A., Martynova, E. U., ... & Goryunova, S. V. (2019). Association mapping of fertility restorer gene for CMS PET1 in sunflower. Agronomy, 9(2), 49.
Chernova, A. I., & Martynova, E. U. (2019). High-throughput technologies for sunflower oil improvement. Current Challenges in Plant Genetics, Genomics, Bioinformatics, and Biotechnology, 24, 227.
