Dynamic Corn yield predictions using Machine Learning on Multi-Source Data for Agri-Food 4.0
Short description of portfolio item number 2 
Posts by Collection
portfolio
publications
1) Ground Truthing and Physiological Validation of Vis-NIR Spectral Indices for Early Diagnosis of Nitrogen Deficiency in cv. Barbera (Vitis vinifera L.) Grapevines
Published in Agronomy, 2019
Recommended citation: Squeri, C., Gatti, M., Garavani, A., Vercesi, A., Buzzi, M., Croci, M., Calegari, F., Vincini, M., & Poni, S. (2019). Ground Truthing and Physiological Validation of Vis-NIR Spectral Indices for Early Diagnosis of Nitrogen Deficiency in cv. Barbera (Vitis vinifera L.) Grapevines. In Agronomy (Vol. 9, Issue 12, p. 864). MDPI AG. https://doi.org/10.3390/agronomy9120864
2) Integration of Gas Exchange With Metabolomics: High-Throughput Phenotyping Methods for Screening Biostimulant-Elicited Beneficial Responses to Short-Term Water Deficit
Published in Frontiers in Plant Science, 2021
Recommended citation: Antonucci, G., Croci, M., Miras-Moreno, B., Fracasso, A., & Amaducci, S. (2021). Integration of Gas Exchange With Metabolomics: High-Throughput Phenotyping Methods for Screening Biostimulant-Elicited Beneficial Responses to Short-Term Water Deficit. In Frontiers in Plant Science (Vol. 12). Frontiers Media SA. https://doi.org/10.3389/fpls.2021.678925
3) Evaluating Biostimulants Via High-Throughput Field Phenotyping: Biophysical Traits Retrieval Through PROSAIL Inversion
Published in Smart Agricultural Technology, 2022
Recommended citation: Antonucci, G., Impollonia, G., Croci, M., Potenza, E., Marcone, A., & Amaducci, S. (2022). Evaluating Biostimulants Via High-Throughput Field Phenotyping: Biophysical Traits Retrieval Through PROSAIL Inversion. Smart Agricultural Technology, 100067. https://doi.org/https://doi.org/10.1016/j.atech.2022.100067
4) Moisture content estimation and senescence phenotyping of novel Miscanthus hybrids combining UAV-based remote sensing and machine learning
Published in GCB Bioenergy, 2022
Recommended citation: Impollonia, G., Croci, M., Martani, E., Ferrarini, A., Kam, J., Trindade, L. M., Clifton-Brown, J., & Amaducci, S. (2022). Moisture content estimation and senescence phenotyping of novel Miscanthus hybrids combining UAV-based remote sensing and machine learning. GCB Bioenergy, 14, 639– 656. https://doi.org/10.1111/gcbb.12930
6) Impact of Training Set Size and Lead Time on Early Tomato Crop Mapping Accuracy
Published in Remote sensing, 2022
Recommended citation: Croci, M.; Impollonia, G.; Blandinières, H.; Colauzzi, M.; Amaducci, S. Impact of Training Set Size and Lead Time on Early Tomato Crop Mapping Accuracy. Remote Sens. 2022, 14, 4540. https://doi.org/10.3390/rs14184540
5) UAV Remote Sensing for High-Throughput Phenotyping and for Yield Prediction of Miscanthus by Machine Learning Techniques
Published in Remote Sensing MDPI, 2022
Recommended citation: Impollonia, G.; Croci, M.; Ferrarini, A.; Brook, J.; Martani, E.; Blandinières, H.; Marcone, A.; Awty-Carroll, D.; Ashman, C.; Kam, J.; Kiesel, A.; Trindade, L.M.; Boschetti, M.; Clifton-Brown, J.; Amaducci, S. UAV Remote Sensing for High-Throughput Phenotyping and for Yield Prediction of Miscanthus by Machine Learning Techniques. Remote Sens. 2022, 14, 2927. https://doi.org/10.3390/rs14122927
7) The effect of agronomic management of hemp (Cannabis sativa L.) on stem processing and fibre quality
Published in Industrial Crops & Products, 2022
Recommended citation: Leoni, M., Musio,S., Croci, M., Tang, K., Magagnini, G., Thouminot, C., Müssig, J., Amaducci, S., The effect of agronomic management of hemp (Cannabis sativa L.) on stem processing and fibre quality
8) RTM Inversion through Predictive Equations for Multi-Crop LAI Retrieval Using Sentinel-2 Images
Published in Agronomy, 2022
Recommended citation: Croci, M.; Impollonia, G.; Marcone, A.; Antonucci, G.; Letterio, T.; Colauzzi, M.; Vignudelli, M.; Ventura, F.; Anconelli, S.; Amaducci, S. RTM Inversion through Predictive Equations for Multi-Crop LAI Retrieval Using Sentinel-2 Images. Agronomy 2022, 12, 2835. https://doi.org/10.3390/agronomy12112835
9) Comparison of PROSAIL Model Inversion Methods for Estimating Leaf Chlorophyll Content and LAI Using UAV Imagery for Hemp Phenotyping
Published in Remote sensing, 2022
Recommended citation: Impollonia, G.; Croci, M.; Blandinières, H.; Marcone, A.; Amaducci, S. Comparison of PROSAIL Model Inversion Methods for Estimating Leaf Chlorophyll Content and LAI Using UAV Imagery for Hemp Phenotyping. Remote Sens. 2022, 14, 5801. https://doi.org/10.3390/rs14225801
10) Agrivoltaic System and Modelling Simulation: A Case Study of Soybean (Glycine max L.) in Italy
Published in Horticulturae, 2022
Recommended citation: Potenza, E.; Croci, M.; Colauzzi, M.; Amaducci, S. Agrivoltaic System and Modelling Simulation: A Case Study of Soybean (Glycine max L.) in Italy. Horticulturae 2022, 8, 1160. https://doi.org/10.3390/horticulturae8121160
11) Dynamic Maize Yield Predictions Using Machine Learning on Multi-Source Data
Published in Remote sensing, 2022
Recommended citation: Croci, M.; Impollonia, G.; Meroni, M.; Amaducci, S. Dynamic Maize Yield Predictions Using Machine Learning on Multi-Source Data. Remote Sens. 2023, 15, 100. https://doi.org/10.3390/rs15010100
12) Yield performance of 14 novel inter- and intra-species Miscanthus hybrids across Europe
Published in GCB-Bioenergy, 2022
Recommended citation: Awty-Carroll, D., Magenau, E., Al Hassan, M., Martani, E., Kontek, M., van der Pluijm, P., … Clifton-Brown, J. (2023). Yield performance of 14 novel inter‐ and intra‐species Miscanthus hybrids across Europe. Global Change Biology. Bioenergy, 15(4), 399–423. doi:10.1111/gcbb.13026
13) Multi-environment assessment of a yellow hemp (Cannabis sativa L.) cultivar’s eco-physiology and productivity under varying levels of nitrogen fertilisation
Published in Industrial Crops and Products, 2022
Recommended citation: Blandinières, H., Croci, M., Impollonia, G., Marcone, A., Gay, A., Winters, A., … Amaducci, S. (2023). Multi-environment assessment of a yellow hemp (Cannabis sativa L.) cultivar’s eco-physiology and productivity under varying levels of nitrogen fertilisation. Industrial Crops and Products, 195(116360), 116360. doi:10.1016/j.indcrop.2023.116360
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Agricoltura di precisione
course, Centro di formazione Dinamica, 2019
This is a description of a teaching experience. You can use markdown like any other post.
Tecnico per il monitoraggio e la gestione del territorio e dell’ambiente
IFTS course, Centro di Formazione Vittorio Tadini, 2020
This is a description of a teaching experience. You can use markdown like any other post.
Tecnico per il monitoraggio e la gestione sostenibile delle risorse idriche
IFTS course, Centro di Formazione Vittorio Tadini, 2021
This is a description of a teaching experience. You can use markdown like any other post.
Esercitazione corso di Modellistica applicata alle coltivazioni erbacee
Laurea Magistrale in Agricoltura sostenibile e di precisione, Facoltà di Agraria, Università Cattolica del Sacro Cuore, Sede di Piacenza, 2023
Argomenti trattati:
- Simulazioni tramite software modello DAISY
- Installazione e configurazione Daisy
- Definizione orizzonti del suolo
- Definizione sistemi di irrigazione
- Confronto diverse gestione dei residui
- Confronto diverse combinazioni fertilizzazione e gestione dei residui
Esercitazione corso di Coltivazioni Erbacee
Laurea triennale in Scienze e Tecnologie Agrarie, Facoltà di Agraria, Università Cattolica del Sacro Cuore, Sede di Piacenza, 2023
Argomenti trattati:
- Fertilizzazione azotata: Esercizi su bilancio analitico
- Irrigazione: Esercizi su bilancio idrico