Hyperspectral Remote Sensing of Potato Plant Nutrient Deprivation and Vegetation Stress using High-Resolution Spectroradiometry for Minimal Input Agricultural Systems

Abstract

Nitrogen is a plant growth limiting nutrient in natural ecosystems, however, many agricultural systems are saturated due to high fertilizer applications. This can lead to higher costs, nitrogen leakage into the environment and unnecessary GHG emissions that contribute to climate change. To avoid this, Neilson (2021) has proposed an approach based on minimal input agricultural systems (MIAS). MIAS seeks to grow crops with limited fertilizer inputs. This is accomplished through targeted/precision fertilizer placement, managing plant physiology to operate at higher efficiencies and adopting new varieties. To work optimally MIAS requires methods to quickly assess plant nutrient status and adjust fertilizer applications accordingly. This study tested remote sensing for detecting stress in potato plants, based on two separate and independent laboratory remote sensing experiments. The goal is to determine minimal input levels applied to a starvation agricultural system that provide yields equivalent (or perhaps even improving upon) those obtained using current excessive inputs, both in terms of yield and, importantly, quality. This research is at the front-end of a proposed paradigm shifting new approach to agriculture. We are being careful to start at first principles in this work; thus the study presented here is based on multiple trials and independent tests. Results testing experimental approaches and N deprivation assessment determined the optimal leaf density and timing of measurements and demonstrated a capability to detect vegetation stress and N deprivation in three potato varieties. These results will inform next steps for future RPAS/UAV/airborne/satellite studies and be used to develop other plant physiology assessment methods.