Presentation at DIGICROP 2025

Jul 8, 2025·
Giorgio Morales
Giorgio Morales
· 1 min read
Image credit: DIGICROP
Abstract
📚 In Precision Agriculture, optimizing fertilizer management is essential for maximizing crop yields and improving agronomic efficiency. Traditional management zone (MZ) approaches focus on within-field variability, but often overlook the impact of fertilizer responsivity on MZ determination. This presentation introduces an MZ clustering method that incorporates fertilizer responsivity, using nitrogen (N) fertilizer-yield response curves. We employ a convolutional neural network to generate N-response curves for each field site, then analyze the shapes of these curves using functional principal component analysis. To refine MZ membership, we apply a genetic algorithm-based counterfactual explanation method that solves a multi-objective optimization problem and identifies the key features influencing cluster assignments. Our results highlight that terrain characteristics, such as slope and topographic aspect, significantly impact MZ membership by affecting fertilizer runoff.
Date
Jul 8, 2025 — Jul 9, 2025
Event
Location

Remote event

🌱 DIGICROP 2025 was a virtual conference bringing together researchers from robotics, computer science, crop science, soil science, phenotyping, economics, environmental science, and more – all focused on advancing sustainability in crop production through digital technologies.