Determining a precise nitrogen fertilizer requirement for maize in a particular field and year has proven to be a challenge due to the complexity of the nitrogen inputs, transformations and outputs in the nitrogen cycle. Remote sensing of maize nitrogen deficiency may be one way to move nitrogen fertilizer applications closer to the specific nitrogen requirement. Six vegetation indices [normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), red-edge normalized difference vegetation index (RENDVI), triangle greenness index (TGI), normalized area vegetation index (NAVI) and chlorophyll index-green (CI_green)] were evaluated for their ability to detect nitrogen deficiency and predict grain maize grain yield. Strip trials were established at two locations in Arkansas, USA, with nitrogen rate as the primary treatment. Remote sensing data was collected weekly with an unmanned aerial system (UAS) equipped with a multispectral and thermal sensor. Relationships among index value, nitrogen fertilizer rate and maize growth stage were evaluated. Green NDVI, RENDVI and CI_green had the strongest relationship with nitrogen fertilizer treatment. Chlorophyll Index-green and GNDVI were the best predictors of maize grain yield early in the growing season when the application of additional nitrogen was still agronomically feasible. However, the logistics of late season nitrogen application must be considered.

Two field experiments were conducted at Kafr El Sheikh Gov., Egypt, during two winter growing seasons (2020/2021 and 2021/2022). The objective of this study was to improve some chemical and physical properties of soil and the yield and water productivity of faba beans (Viciafaba L.), Cv. Sakha-4 by application of gypsum, compost, and some nanoparticles in salt-affected soils. The experimental treatments were arranged in a split-plot design with three replications. The main plots had the following soil amendments: T₁: control treatment, T₂: 10 tons compost/hectare, T₃: soil gypsum requirement (GR) of 8.59 ton ha⁻¹, and T₄: GR + 10 tons compost/hectare. The subplots were treated with foliar application as follows: no treatment, manganese nanoparticles (Mn-NPs), selenium nanoparticles (Se-NPs), and Mn-NPs + Se-NPs. According to the findings, the application of compost + GR significantly decreased soil salinity (EC), exchangeable sodium percentage (ESP), and soil bulk density (BD). However soil porosity, soil penetration resistance (SPRa), and basic soil infiltration (IR) were significantly increased. On the other hand, the results revealed significant positive effects onthe 100-grain weight as well as proline, chlorophyll, superoxide dismutase, and catalase contents due to the interaction between gypsum + compost and Mn-NPs + Se-NPs, which enhanced the productivity of both the seed and straw yields of faba beans compared to the alternative treatments. In addition, the seed yield and irrigation water productivity (PIW, kg m³) of faba beans were significantly increased with addition of gypsum and compost and foliar application of nanoparticles. The highest values of these parameters were achieved due to the interaction between gypsum + compost and Mn-NPs + Se-NPs. It can be concluded that application of GR of 8.59 ton ha⁻¹ and 10 ton ha⁻¹ compost as well as foliar application of Mn-NPs and Se-NPs may be a key strategy for improving some chemical and physical properties of soil and the yield and water productivity of faba beans in salt-affected soil under these experimental conditions.

Evapotranspiration (ET) is one of the biggest data gaps in water management due to limited ET measurements, and further, spatial variability in ET is difficult to capture. Satellite-based ET estimation has great potential for water resources planning as it allows estimation of agricultural water use at field, landscape, and watershed scales. However, uncertainties with satellite data derived ET are a major concern. This study evaluates hourly satellite-based ET from 2001–2010 for the growing season (May–October) under irrigated and dryland conditions for both tall and short crops. The evaluation was conducted using observed ET from four large weighing lysimeters at the United States Department of Agriculture Agricultural Research Service (USDA-ARS) Conservation and Production Research Laboratory in Bushland, Texas. Hourly ET from satellite data were derived using the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model. Performance statistics showed that satellite-based hourly estimates compared to lysimeter measurements provided good performance with an root-mean-square error(RMSE) of 0.14 mm, Nash–Sutcliffe efficiency (NSE) of 0.57, and R² of 0.62 for ET for dryland crops, and RMSE of 0.16, NSE of 0.63, and R² of 0.65 for irrigated crops. METRIC provided accurate hourly ET estimates that may be useful for irrigation scheduling and other water resources management purposes based on the hourly assessment.

Timeseries data captured by unoccupied aircraft systems (UASs) are increasingly used for agricultural applications requiring accurate prediction of plant phenotypes from remotely sensed imagery. However, prediction models often fail to generalize well from one year to the next or to new environments. Here, we investigate the ability of various machine learning (ML) approaches to improve yield prediction accuracy in new environments from multispectral timeseries imagery acquired on a set of rice (Oryza sativa L.) experiments with different management treatments and varieties. We also trained deep learning models that perform automated feature extraction and compared these against a suite of other approaches. We observed similar performance on a held‐out growing season for a spatiotemporal model (a three‐dimensional convolutional neural network) trained on raw images compared to simpler workflows using dimension reduction of manually extracted features from temporal imagery (i.e., vegetation indices and image texture properties). Manifold learning on raw imagery was better suited for the prediction of phenological traits due to the preservation of local structure in image embeddings at some time points. Together, these results highlight the competitiveness of classical ML approaches for UAS image analysis alongside computationally expensive deep learning models. Along with a new benchmark dataset for rice, our results help extend the toolkit for UAS image analysis, contributing to improved phenotype prediction in plant breeding and precision agriculture applications.

Advances in remote sensing and small unmanned aircraft systems (sUAS) have been applied to various precision agriculture applications. However, there has been limited research on the accuracy of real-time kinematic (RTK) sUAS photogrammetric elevation surveys, especially in preparation for precision agriculture practices that require precise topographic surfaces, such as increasing irrigation system efficiency. These practices include, but are not limited to, precision land grading, placement of levees, multiple inlet rice irrigation, and computerized hole size selection for furrow irrigation. All such practices rely, in some way, on the characterization of surface topography. While agro-terrestrial (ground-based) surveying is the dominant method of agricultural surveying, aerial surveying is emerging and attracting potential early adopters. This is the first study of its kind to assess the accuracy, precision, time, and cost efficiency of RTK sUAS surveying in comparison to traditional agro-terrestrial techniques. Our findings suggest sUAS are superior to ground survey methods in terms of relative elevation and produce much more precise raster surfaces than ground-based methods. We also showed that this emergent technology reduces costs and the time it takes to generate agricultural elevation surveys.