An integrative data-driven approach for monitoring corn biomass under irrigation water and nitrogen levels based on UAV-based imagery.

Unmanned aerial vehicle (UAV)-based remote sensing has been widely considered recently in field scale crop yield estimation. In this research, the capability of 13 spectral indices in the form of 5 groups was studied under different irrigation water and N fertilizer managements in terms of corn biomass monitoring and estimation. Farm experiments were conducted at Urmia University, Iran.

Long-term numerical modeling of nitrate leaching into groundwater under surface drip irrigation of corn.

Proper management of fertigation is necessary to deal with the harmful impacts of fertilizers. This research aimed to investigate the nitrate leaching rate into groundwater in different fertigation management under the climate change impact in drip irrigation of corn. For this purpose, HYDRUS-2D was calibrated by performing field experiments.

A hybrid machine learning approach for estimating the water-use efficiency and yield in agriculture.

This paper introduces the narrow strip irrigation (NSI) method and aims to estimate water-use efficiency (WUE) and yield in apple orchards under NSI in the Miandoab region located southeast of Lake Urmia using a machine learning approach. To perform the estimation, a hybrid method based on an adaptive neuro-fuzzy inference system (ANFIS) and seasons optimization (SO) algorithm was proposed. According to the irrigation and climate factors, six different models have been proposed to combine the parameters in the SO-ANFIS.

Linking plant and soil indices for water stress management in black gram.

Measurement of plant and soil indices as well as their combinations are generally used for irrigation scheduling and water stress management of crops and horticulture. Rapid and accurate determination of irrigation time is one of the most important issues of sustainable water management in order to prevent plant water stress. The objectives of this study are to develop baselines and provide irrigation scheduling relationships during different stages of black gram growth, determine the critical limits of plant and soil indices, and also determine the relationships between plant physiology and soil indices.

An analysis of optimal fertigation implications in different soils on reducing environmental impacts of agricultural nitrate leaching.

Excessive and incorrect use of nitrogen (N) fertilizers in agriculture leads to high nitrate leaching to groundwater and harmful effects on the environment. The main objective of this research was to optimize the N fertigation scheduling for a surface micro-irrigation system in different soils. N uptake by corn and its losses were investigated for two fertigation scheduling scenarios including regional recommendation scheduling with three fertigation events and a weekly application schedule.

Evaluation of fertigation management impacts of surface drip irrigation on reducing nitrate leaching using numerical modeling.

The objective of this study was to investigate the impacts of fertigation strategies on nitrate leaching and its uptake into maize plants. Field experimental data were employed to calibrate a numerical model (HYDRUS 2D/3D) for a surface drip irrigation system in a sandy clay loam soil. The calibrated model was used to simulate nitrate plant uptake and its leaching in different fertigation scenarios based on various fertigation durations and different start times of fertigation.