Nicolas Federico Martin

Modeling yield response to crop management using convolutional neural networks

Computers and Electronics in Agriculture · 2020 · 98 citations

Predicting crop yield response to management and environmental variables is a crucial step towards nutrient management optimization. With the increase in the amount of data generated by agricultural machinery, more sophisticated models are necessary to get full advantage of such data. In this work, we propose a Convolutional Neural Network (CNN) to capture relevant spatial structures of different attributes and combine them to model yield response to nutrient and seed rate management. Nine on-fa…

Spatial variability of crop responses to agronomic inputs in on-farm precision experimentation

Precision Agriculture · 2020 · 72 citations

Abstract Within-field variability of crop yield levels has been extensively investigated, but the spatial variability of crop yield responses to agronomic treatments is less understood. On-farm precision experimentation (OFPE) can be a valuable tool for the estimation of in-field variation of optimal input rates and thus improve agronomic decisions. Therefore, the objectives of this study were to investigate the spatial variability of optimal input rates in OFPE and the potential economic benefi…

Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · 2022-06-01 · 44 citations

Nitrogen (N) management is critical to sustain soil fertility and crop production while minimizing the negative environmental impact, but is challenging to optimize. This paper proposes an intelligent N management system using deep reinforcement learning (RL) and crop simulations with Decision Support System for Agrotechnology Transfer (DSSAT). We first formulate the N management problem as an RL problem. We then train management policies with deep Q-network and soft actor-critic algorithms, and…

Evaluation of survey and remote sensing data products used to estimate land use change in the United States: Evolving issues and emerging opportunities

Environmental Science & Policy · 2021-12-28 · 35 citations

Transparent, consistent, and statistically reliable land use/ land cover area estimates are needed to assess land use change and greenhouse gas emissions associated with biofuel production and other land uses that are influenced by policy. As relevant studies have increased rapidly during past decades, the methods used to combine data extracted from land use land cover (LULC) surveys and remote sensing-based products and track or report sources of uncertainty vary notably. This paper reviews six…

On to the next chapter for crop breeding: Convergence with data science

Crop Science · 2020 · 34 citations

Abstract Crop breeding is as ancient as the invention of cultivation. In essence, the objective of crop breeding is to improve plant fitness under human cultivation conditions, making crops more productive while maintaining consistency in life cycle and quality. Predictive breeding has been demonstrated in the agricultural industry and in public breeding programs for over a decade. The massive stores of data that have been generated by industry, farmers, and scholars through several decades have…