Soil Water Content and High-Resolution Imagery for Precision Irrigation: Maize Yield

Abstract

Improvement in water use efficiency of crops is a key component in addressing the increasing global water demand. The time and depth of the soil water monitoring are essential when defining the amount of water to be applied to irrigated crops. Precision irrigation (PI) is a relatively new concept in agriculture, and it provides a vast potential for enhancing water use efficiency, while maintaining or increasing grain yield. Neutron probes (NPs) have consistently been used as a robust and accurate method to estimate soil water content (SWC). Remote sensing derived vegetation indices have been successfully used to estimate variability of Leaf Area Index and biomass, which are related to root water uptake. Crop yield has not been evaluated on a basis of SWC, as explained by NPs in time and at different depths. The objectives of this study were (1) to determine the optimal time and depth of SWC and its relationship to maize grain yield (2) to determine if satellite-derived vegetation indices coupled with SWC could further improve the relationship between maize grain yield and SWC. Soil water and remote sensing data were collected throughout the crop season and analyzed. The results from the automated model selection of SWC readings, used to assess maize yield, consistently selected three dates spread around reproductive growth stages for most depths (p value < 0.05). SWC readings at the 90 cm depth had the highest correlation with maize yield, followed closely by the 120 cm. When coupled with remote sensing data, models improved by adding vegetation indices representing the crop health status at V9, right before tasseling. Thus, SWC monitoring at reproductive stages combined with vegetation indices could be a tool for improving maize irrigation management.

Optical Satellite Images Services for Precision Agricultural use: A Review

“…The main application had permitted the detection of agricultural disease and its extent to establish the suitable way of intervention and the adequate amount of pesticide to apply [5,6]. Other applications concern the determination of water requirement at the plant scale for precision irrigation [3,7,8] as well as the determination of soil fertility [9], yield and biomass production [2,10] and others.…”

Assessing Effects of Salinity on the Performance of a Low-Cost Wireless Soil Water Sensor

“…Soil water content (SWC) is a key variable for driving plant growth, precision irrigation management [ 1 , 2 ], and coupled hydrological, environmental, climatological, and ecohydrological processes [ 3 ]. SWC plays a significant role in hydrology to understand the hydrological cycle [ 4 ], rainfall patterns [ 5 ], interflow [ 6 ], and groundwater recharge [ 7 ].…”

Real-Time Localization Approach for Maize Cores at Seedling Stage Based on Machine Vision

“…Implementing weeding, spraying, and navigation work through recognition and localization for crops, such as maize, has long been a research focus regarding the technology and equipment of precise agriculture [2][3][4]. The predecessors have distinguished weeds through localizing position of crops with machine vision technology [5][6][7], to implement weeding work [8,9] and determine crop diseases and position for spraying.…”