Search Results (653)

The rapid aging of the agricultural workforce underscores the need for technologies that ensure both productivity and usability. This study introduces an AIoT-enabled elderly-friendly greenhouse that integrates ergonomic and agronomic parameters into a unified optimization framework. Experiments with ten elderly participants (60–75 years) combined anthropometric assessments, environmental monitoring, and machine learning–based irrigation modeling. Results showed that an optimal planting table height of 75 cm maximized comfort (4.44 ± 0.34) and minimized fatigue (1.89 ± 0.66). Work–rest scheduling identified early morning (06:00–09:00) and late afternoon (15:00–18:00) as periods with reduced heat strain. Ventilation at 60% fan speed-maintained comfort ranges while stabilizing microclimate conditions. For irrigation, Random Forest Regression achieved the best accuracy (R² ≈ 0.75), with soil moisture as the dominant predictor. A Genetic Algorithm (GA) further improved outcomes, increasing comfort scores by 30% and reducing water use by 20%. By embedding ergonomic (X_opt, T_comfort, V_comfort) and agronomic (W, I, θ_opt) variables as objectives, the system creates greenhouses that are both “user-aware” and “plant-aware.” This dual approach enhances productivity, sustainability, and usability, offering practical insights for AIoT-enabled smart greenhouses in aging societies. Full article

Despite its significant water-saving potential, the adoption of alternate wetting and drying (AWD) irrigation remains limited due to infrastructure constraints and intensive manual monitoring requirements. An automated precision irrigation system was developed and tested at the Bangladesh Rice Research Institute research farm in Gazipur, Bangladesh. The system combined ultrasonic water-level sensors, capacitive soil moisture sensors, an Arduino-based microcontroller, a GSM communication module, and solar-powered automatic control gates. Field performance was evaluated following a Randomized Complete Block Design (RCBD) under four irrigation treatments: IRRISAT, IRRI35, IRRI25, and continuous flooding (CF). The first three irrigation treatments were operated using scheduled daily decision windows, in which irrigation actions were automatically triggered based on predefined schedules and sensor threshold values. In IRRISAT, irrigation started when soil moisture dropped slightly below saturation and stopped at a ponding depth of 5 cm, while IRRI35 and IRRI25 were triggered at volumetric soil water contents of 35% and 25%, respectively, with the same upper cutoff of 5 cm ponding depth; CF served as the control. The IRRI35 treatment achieved a high grain yield (7.76 t ha⁻¹) while reducing water use by 28% and energy consumption by 37% compared to CF. Water use efficiency was considerably higher under IRRI35 (9.4 kg ha⁻¹ mm⁻¹) than under CF (6.7 kg ha⁻¹ mm⁻¹). The automated system proved to be reliable and precise in scheduled irrigation control, significantly reducing water use and labor requirements. The findings suggest that large-scale adoption of the system under real-world cultivation conditions could reduce irrigation energy needs and contribute to sustainable water governance in rice production. Full article

Water scarcity is the primary constraint on the development of the potato industry in Northwest China. Improving water use efficiency (WUE) under limited water supply is, therefore, an urgent priority to promote the green and sustainable development of potato production in this region. This research was conducted from 2023 to 2024 in the rain shelter of the Agricultural Science Research Institute in Dingxi City, Gansu Province, using the potato cultivar ‘Gan Yin No. 9’ as the experimental material. Throughout the growing season, the control treatment (CK) was maintained at 75–85% of the field water capacity (FWC). Based on CK, three deficit-irrigation treatments were established: W₇₅ (75% of the CK irrigation amount), W₅₀ (50% of CK irrigation amount), and W₂₅ (25% of CK irrigation amount), with three replicates per treatment. We evaluated the effects of different irrigation regimes on plant growth characteristics, physiological characteristics, tuber yield, and WUE. The results showed that the W₇₅ treatment significantly (p < 0.05) promoted the growth of plant height and stem diameter, and significantly increased them by 8.70–10.20% and 13.03–18.70%, respectively, compared with CK. The total dry matter accumulation under W₇₅ was significantly higher than CK (by 10.90–11.40%) and markedly higher than W₅₀ and W₂₅ (by 24.10–45.50%). No significant differences were observed in tuber yield, large tuber rate, and medium tuber rate between W₇₅ and CK. Notably, W₇₅ significantly improved WUE by 36.43–38.51% compared with CK. Overall, under the conditions of this study, W₇₅ treatment was identified to be the optimal irrigation regime for potato cultivation, as it promoted plant growth, maintained tuber yield, and enhanced water use efficiency. This study aims to establish a definitive irrigation threshold for potato production in Northwest China. The findings provide a precise basis for formulating irrigation schedules, which can contribute to the development of water-efficient agriculture and support the sustainable development of the potato industry in the region. Full article

Sustainable vegetable production requires strategies that optimize yield while conserving water and minimizing resource inputs. This study, conducted at the Horticulture Research Farm near Absaraka, ND, evaluated the performance of several paste-type tomato (Solanum lycopersicum) cultivars under different irrigation strategies in high-tunnel and open-field production systems to identify cultivar and irrigation combinations that support sustainable production. Across seasons and production environments, cultivar significantly influenced marketable yield, fruit number, fruit size, and the proportion of unmarketable fruit, whereas irrigation treatments had limited effects on total and marketable yield. High-yielding cultivars such as ‘Granadero’, ‘Pozzano’, ‘Cauralina’, and ‘Amish Paste’ consistently produced greater marketable yields in both production systems, although ‘Cauralina’ also exhibited higher levels of fruit cracking and unmarketable yield. In high-tunnel production, deficit irrigation strategies based on soil moisture thresholds (10% and 30% management allowable depletion) maintained yields comparable to time-based irrigation, suggesting that water-efficient irrigation scheduling can sustain productivity. In the open field, cultivar responses varied under different irrigation regimes, highlighting the importance of selecting cultivars adapted to water-limited conditions. Fruit quality attributes, including soluble solids content and titratable acidity, were primarily influenced by cultivar rather than irrigation. Overall, the findings demonstrate that cultivar selection combined with water-efficient irrigation management can maintain tomato productivity while reducing water use and production losses. These results support the development of more sustainable tomato production systems that enhance resource-use efficiency, reduce waste from unmarketable fruit, and maintain fruit quality across diverse production environments. Full article

To address peak edge operation and excessive valve switching in hydraulically coupled multi-zone campus irrigation, this study proposes a collaborative scheduling framework that combines short-term evapotranspiration (ET) forecasting with safety-constrained reinforcement learning. Temperature, relative humidity, and light intensity are used to construct vapor pressure deficit and radiation proxy features, and a lightweight predictor provides two-hour-ahead ET statistics as forward-looking disturbance information. A safety layer composed of Top-2 gating and total flow projection is then used to map policy outputs into a feasible action space under parallel irrigation and total flow constraints. Using seven consecutive days of field data from October 2025, the proposed method reduced total water consumption to 131.04 m³, corresponding to reductions of 9.13% and 6.12% relative to fixed-schedule and hysteresis threshold rotational irrigation, respectively. It also reduced the maximum total flow from 2.00 to 1.60 L/s, lowered valve switching cycles to 12, and reduced the border ratios at 0.90 and 0.95 to 0. Additional ablation, sensing noise/packet loss, and Top-K extension experiments further showed that ET forecasting improves anticipatory scheduling, whereas safety projection is essential for zero-violation operation. These results demonstrate that the proposed framework provides a practical and deployable solution for safe and water-efficient multi-zone irrigation scheduling under shared pump constraints. Full article

Crop simulation models and irrigation decision support systems (IDSS) are essential tools for improving water use efficiency, particularly in Mediterranean and semi-arid regions where water scarcity is a major constraint. However, many platforms are either too complex for widespread adoption or too simplified to capture the combined effects of temperature, water stress, and elevated CO₂ on crop responses. This paper presents the Easy Simulator Crop Model (EaSiCroM), a modular, low-parameterisation system designed to simulate daily crop growth, soil water dynamics, and irrigation requirements. Canopy development follows a beta-function LAI trajectory with Beer–Lambert canopy cover, progressively constrained by temperature (T_lim) and water stress (K_stress, KS_cc). Biomass accumulation combines a water productivity (WP) approach with an optional radiation-use efficiency (RUE) pathway, both scaled by a Michaelis–Menten CO₂ fertilisation sub-model. The soil water balance includes a two-stage bare-soil evaporation formulation and multiple irrigation triggering strategies. EaSiCroM is implemented as a Docker-containerised web application supporting single-crop, multi-plot, and near-real-time irrigation modes, with optional assimilation of user-provided canopy observations from field or remote sensing sources. A proof-of-concept evaluation across four Mediterranean crops (processing tomato, biomass sorghum, sunflower, and durum wheat) yielded RRMSE values between 13.8% and 26.1%, comparable to AquaCrop and CropSyst on the same datasets. Its modular architecture makes it suitable for both research and operational irrigation management in water-scarce environments. Full article

Climate change is intensifying hydroclimatic extremes and agricultural water scarcity, sharpening trade-offs among yield stability, water saving, and farm incomes in major grain regions. Existing studies often optimise cropping patterns or irrigation schedules separately, seldom embedding yield robustness and policy instruments in one decision framework. We propose an integrated Machine-learning–System-dynamics–Non-dominated-sorting-genetic-algorithm-II (ML–SD–NSGA-II) framework linking long-horizon meteorological scenario generation, crop–water–economy feedback and multi-objective optimisation of crop areas and irrigation depths. ML models generate daily climate sequences to drive an SD model of soil moisture, yield formation, basin-scale allocable water, and farm returns; NSGA-II searches Pareto-optimal strategies that maximise profit and irrigation water productivity while minimising yield deviation. Applied to a rice–wheat irrigation system in the middle Yangtze River Basin, knee-point solutions lift irrigation water productivity by about 14%, maintain near-baseline profits, and reduce yield deviation. Scenario tests with block tariffs, quota-based subsidies, and extreme drought show pricing mainly curbs low-value water use in normal years, while under drought, physical scarcity dominates and economic tools offer limited buffering. This reveals the existence of a scarcity-regime threshold beyond which economic instruments become second-order relative to binding biophysical constraints. The framework supports transparent ex ante testing of tariff–subsidy packages for irrigation governance and adaptation. Full article

As water resources are becoming increasingly scarce in the North China Plain, irrigation strategies that simultaneously improve grain yield and reduce irrigation water input are needed for winter wheat (Triticum aestivum L.) production. Current irrigation decision rules are based either on fixed soil moisture thresholds or on evapotranspiration (ET)-based ratios applied uniformly across the growing season, limiting their flexibility for growth stage-specific irrigation management. In this study, a multi-objective simulation optimization framework was developed to jointly optimize soil moisture lower control limits (irrigation trigger thresholds) and evapotranspiration-based irrigation replenishment ratios across key winter wheat growth stages. The framework integrated the AquaCrop-OSPy crop model with the PyFAO56 soil moisture balance, irrigation scheduling model and the NSGA-II evolutionary optimization algorithm. A field experiment was conducted during the 2024–2025 growing season in Laoling City, Shandong Province, China, employing a four-dense–one-sparse strip cropping pattern with two irrigation treatments: T1 (subsurface sprinkler irrigation) and T2 (shallow subsurface drip irrigation). The AquaCrop-OSPy model was calibrated and validated using measured canopy cover, aboveground biomass, grain yield, and soil moisture content in the 0–60 cm soil layer. Simulated canopy cover and grain yield showed good agreement with observations, with the coefficient of determination (R²) ranging from 0.87 to 0.94. For grain yield, the normalized root mean square error (NRMSE) ranged from 2.24% to 3.75%, and the root mean square error (RMSE) ranged from 0.29 to 0.54 t·ha⁻¹. For aboveground biomass, R² was 0.99, while RMSE ranged from 1.02 to 1.11 t·ha⁻¹, and NRMSE ranged from 14.25% to 15.49%. The PyFAO56 irrigation strategy model simulated average root-zone soil-moisture dynamics with satisfactory accuracy, with an R² of 0.86 and an RMSE of 5%. Multi-objective optimization (maximizing yield while minimizing irrigation volume) generated 23 Pareto-optimal irrigation strategies, with irrigation volumes ranging from 51 to 128 mm, corresponding yields ranging from 9.8 to 10.8 t·ha⁻¹, and irrigation water use efficiency (IWUE) ranging from 0.08 to 0.19 t·ha⁻¹·mm⁻¹. Correlation analysis within the Pareto set indicated that soil-moisture control lower limits during the regreening–jointing stage and higher soil-moisture control lower limits during the flowering–maturity stage were key controlling factors for achieving high yields and irrigation water use efficiency. The Entropy-Weighted Ranked Minimum Distance method identified an optimal irrigation scheme involving two irrigations (one at the end of the jointing stage and another at the beginning of the grain filling stage) involving an irrigation depth of 75 mm, achieving a simulated yield of 10.4 t·ha⁻¹ and an IWUE of 0.16 t·ha⁻¹·mm⁻¹. The proposed AquaCrop-PyFAO56-NSGA-II framework provides a flexible, process-based workflow for jointly optimizing irrigation control thresholds and evapotranspiration-based irrigation replenishment ratios across different winter wheat growth stages. Under the monitored conditions of the 2024–2025 wet season, the framework identified a two-irrigation strategy that balanced grain yield and irrigation input. This study should, therefore, be regarded as a proof-of-concept evaluation conducted in a well-instrumented single-site field setting rather than as a universally transferable recommendation. Because model calibration, within-season validation, and optimization were all based on one wet growing season at one site, the derived stage-specific thresholds, Pareto front, and S5 recommendation are most applicable to hydro-climatic conditions similar to the study year and should be further tested across contrasting year-types and locations before broader extrapolation. Full article

With the advancement of agricultural modernization, issues related to resource conservation, intensive utilization, and green, low-carbon development have become increasingly prominent. To enhance water and fertilizer use efficiency in Henan Province and promote green, low-carbon, and sustainable agricultural development, field experiments were conducted during 2023–2024. The experiment employed a randomized complete block design with three replications. Each plot measured 30 m² (5 m × 6 m), totaling 36 plots. An IoT-based real-time coordinated water-fertilizer regulation technology, driven by continuous WSH-TDR310S sensor monitoring of soil moisture and nitrogen status with automated threshold-based control logic, was implemented. By transforming the traditional static scheduling approach into a dynamic feedback mechanism driven by real-time sensor data, the synchronization between resource supply and crop demand was achieved. This study aimed to elucidate the response characteristics of summer maize growth dynamics and farmland N₂O emissions under the proposed regulation strategy. The experiment included three levels of water deficit (mild, moderate, and severe) and three fertilization levels (low, medium, and high), resulting in a total of nine real-time water–fertilizer coordinated regulation treatments, along with three local border irrigation control treatments. The results showed that under real-time water–fertilizer regulation, plant height, stem diameter, and leaf area index of summer maize exhibited unimodal variation patterns, with the medium irrigation–medium fertilization (B2) treatment performing optimally. Compared with the border-irrigation medium-fertilization control (D2), plant height and stem diameter under the B2 treatment increased significantly. Cumulative farmland N₂O emissions increased with higher irrigation and fertilization levels, with the border-irrigation high-fertilization treatment producing the highest emissions. Yield formation was mainly governed by structural growth traits, with plant height showing the strongest predictive ability, followed by stem diameter, whereas leaf area index showed weaker explanatory power. Summer maize yield exhibited a unimodal response to both irrigation and nitrogen input levels. Compared with the D2 treatment, the B2 treatment increased grain yield by 41.33%, while achieving water-saving and fertilizer-saving rates of 38.10% and 35.75%, respectively, thereby achieving an optimal balance between high yield and efficient water–fertilizer utilization. These findings provide theoretical support for summer maize production in the North China Plain and contribute to the promotion of green and sustainable agricultural development. Full article

In arid and semi-arid regions, improving water use irrigation efficiency under limited seasonal water supply is critical for sustainable cotton production. While the effects of seasonal irrigation amount have been widely studied, the independent and interactive roles of irrigation interval under a fixed seasonal irrigation constraint remain insufficiently quantified. This study aimed to evaluate how irrigation amount and interval jointly regulate soil water dynamics, evapotranspiration partitioning, yield formation, and water use efficiency (WUE) in cotton. A two-year, controlled soil-column experiment was conducted using a full-factorial design with two seasonal irrigation amounts (350 and 200 mm) and four irrigation intervals (every 3, 6, 9, or 12 days). The AquaCrop model was locally calibrated with 2024 data and validated with independent 2025 observations. The validated model was then used to conduct scenario simulations across 16 irrigation combinations to analyze process-level responses. The model reproduced canopy cover and soil water storage (SWS) dynamics with good accuracy (R² > 0.89; NRMSE < 16%). The results showed that irrigation amount primarily controlled overall water availability, whereas irrigation interval reshaped the temporal fluctuation pattern of soil water content (SWC) in the shallow root zone. Under moderate irrigation levels, an intermediate interval (every 6 days) improved WUE by stabilizing SWC and maintaining high transpiration proportions. Under severe deficit conditions, prolonged intervals intensified periodic water stress and reduced yield. Simulated transpiration accounted for 95–97% of seasonal evapotranspiration in the controlled system, reflecting limited soil evaporation under column conditions. These findings highlight that irrigation interval, beyond total irrigation amount, is an important management variable for optimizing cotton irrigation scheduling under water-limited conditions. The combined experimental and modeling framework provides practical guidance for irrigation design in arid regions. Full article

This study evaluated the performance of a smart farming technology (SFT) and a climate-smart agriculture (CSA) approach for improving irrigation management in pecan (Carya illinoinensis) orchards in México through soil moisture monitoring, evapotranspiration estimation, and real-time data integration. Continuous monitoring allowed irrigation to be maintained at field capacity, preventing plant stress while avoiding total soil saturation or permanent wilting point. Calibration of soil moisture sensors showed a very strong correlation (R² = 0.99) between sensor reverse voltage and volumetric soil water content in predominant sandy loam soils, confirming the reliability of the monitoring system for irrigation scheduling. Seasonal analysis of reference evapotranspiration (ETo) and crop evapotranspiration (ETc) revealed increasing atmospheric water demand during summer months, with crop coefficient (Kc) values ranging from approximately 0.3 during dormancy to 1.0–1.3 during peak vegetative growth. After five years of field implementation of the technology, results showed water savings exceeding 50% compared with traditional flood irrigation practices. The optimized irrigation schedule reduced total seasonal irrigation depth from 216 cm to 128 cm, representing a 59% reduction in applied water while maintaining adequate soil moisture conditions for crop development at field capacity (FC). These results highlight the potential of integrating sensor-based monitoring, evapotranspiration modeling, and IoT platforms to enhance water-use efficiency and support sustainable pecan production under increasing climate variability. Full article

Industrial hemp (Cannabis sativa L.) is versatile and rapidly developing, offering new prospects to producers as a multipurpose crop, yet limited water availability in the Mediterranean area due to climate change makes its sustainable management challenging. Although the plant’s water requirements have been studied, a significant gap remains regarding irrigation management based on the hydraulic properties that govern water movement. The present study elucidates the role of soil hydraulic parameters in water dynamics within the rhizosphere of industrial hemp (Cannabis sativa L., ‘Felina 32’). For this purpose, a pot experiment of three irrigation treatments (100% FC, 80% FC, 60% FC; FC is the field capacity) was set up using two different soil types (clay loam CL and sandy clay loam SCL). SCL soil showed a higher C_max of about 4 cm⁻¹ compared to the C_max of 0.11 cm⁻¹ of CL soil, but dropped drastically within a narrow frame of soil moisture. CL soil resulted in about 12-fold higher diffusivity (D_max ≈ 0.23 cm² min⁻¹) within a wider range of soil moisture compared to the SCL soil (D_max ≈ 0.02 cm² min⁻¹), which facilitated water redistribution at CL, allowing the plant to maximize its water uptake, even at the lowest water input. As a result, the CL soil allowed more flexible scheduling and in contrast, SCL soil necessitated a high frequency irrigation protocol. The integration of hydraulic properties into irrigation planning revealed the potential of CL to apply water to plants efficiently across full and deficit irrigation, showing the peak performance of the irrigation water use efficiency (IWUE) (0.929 g/mm) under the 60% FC regime. The findings provide a framework for linking soil physics–agricultural hydraulics with irrigation strategies in controlled environments. Full article

Evapotranspiration and crop coefficients are key variables for designing efficient irrigation strategies in tree crops, yet standard tabulated coefficients derived for mature, fully covering orchards often fail to represent the water use of young, high-density hazelnut systems. In recent years, updated crop coefficients for temperate fruit trees, including hazelnut, and transpiration-based models have been proposed, while several studies have successfully linked Vegetation Indices and thermal metrics to single and basal crop coefficients in vineyards, orchards and field crops. However, no information is available on the use of UAV-derived spectral and thermal indices to estimate crop coefficients in high-density hazelnut orchards. This study compares crop coefficients obtained from traditional approaches (the FAO56 single crop coefficient, a transpiration-based coefficient, and ground cover reduction factors) with coefficients estimated from UAV-derived Normalized Difference Water Index (NDWI) and Crop Water Stress Index (CWSI) in a subsurface-drip-irrigated hazelnut orchard (cv. Tonda Francescana^®) with two planting densities (625 and 1250 trees ha⁻¹) in central Italy. Multispectral and thermal UAV surveys carried out between 2021 and 2024 were used to derive canopy geometrical traits, ground cover, NDWI, and CWSI, while a local weather station provided reference evapotranspiration. Empirical relationships were calibrated between crop coefficients and ground cover, NDWI, and CWSI, and mid-season coefficients were applied to estimate daily crop evapotranspiration, which was then compared with the irrigation volumes supplied during the 2024 season. The standard FAO56 crop coefficient (Kc = 0.9) overestimated evapotranspiration, especially at the lower planting density, whereas ground cover-based reduction factors recalibrated for hazelnut and the transpiration-based coefficient provided estimates more consistent with the applied irrigation. UAV-based NDWI- and CWSI-derived crop coefficients produced mid-season values close to those obtained with the transpiration-based method for both planting densities, confirming that spectral and thermal information can effectively capture the combined effects of canopy development and water status. These results indicate that combining traditional methods with UAV-derived indices offers a flexible framework to refine crop coefficients in high-density hazelnut orchards and support more accurate and spatially explicit irrigation scheduling. Full article

Precision Agriculture (PA) is increasingly applied to nutraceutical cropping systems, where agronomic productivity must be integrated with the stabilization of phytochemical quality and environmental sustainability. This structured narrative review synthesizes scientific evidence (primarily 2010–2025) on the use of Unmanned Aerial Vehicle (UAV)-based multispectral and thermal sensing, LiDAR-derived canopy characterization, Internet of Things (IoT) monitoring, and artificial intelligence (AI)-driven analytics in medicinal, aromatic, and functional crops. The literature indicates that PA enhances high-resolution monitoring of crop–environment interactions, supporting site-specific irrigation, nutrient management, and stress detection. Under validated conditions, these interventions are associated with improved yield stability, resource-use efficiency, and modulation of secondary metabolite accumulation. However, reported outcomes vary substantially across species, agroecological contexts, and experimental scales, and most studies remain plot-scale or pilot-scale, limiting large-scale generalization. Moringa oleifera Lam. is examined as a model species for Mediterranean and semi-arid systems. Evidence suggests that integrated spectral, structural, and environmental monitoring can support optimized irrigation scheduling, canopy uniformity, and phytochemical consistency. Nonetheless, genotype-specific calibration, multi-season validation, standardized metabolomic benchmarking, and cross-regional transferability remain significant research gaps. Overall, PA represents a scientifically promising but still maturing framework for nutraceutical agriculture. Future progress will require rigorous multi-site validation, improved model robustness, standardized sustainability metrics, and comprehensive economic assessments to ensure scalability and long-term impact. Full article

Soil moisture prediction is essential for precision irrigation in water-limited agricultural systems. This study presents a deep learning-driven irrigation framework for winter wheat, integrating a novel Informer–UNet model with a Comprehensive Irrigation Index for adaptive water management. The Informer–UNet combines ProbSparse self-attention mechanisms with UNet’s multi-scale feature fusion, enabling simultaneous prediction of soil moisture at 27 monitoring points across three depths, 10, 30, and 50 cm, while quantifying prediction uncertainty through Monte Carlo Dropout. A Comprehensive Irrigation Index incorporating moisture deviation, spatial variance, and confidence interval width was developed, with weights optimized via genetic algorithm. Field experiments were conducted in Chengdu, China, over two winter wheat growing seasons. The Informer–UNet achieved superior prediction accuracy, R² greater than 0.98, RMSE less than 0.65, compared to LSTM, Transformer, and standard Informer models, with the fastest convergence and lowest validation loss. The proposed DeepIndexIrr strategy maintained soil moisture within the target range, 55% to 75%, for over 81% of the irrigation period, reducing water consumption by 38.2% compared to fixed-threshold control and 19.2% compared to expert manual scheduling. These results demonstrate that integrating spatially distributed deep learning predictions with uncertainty-informed decision rules offers a promising approach for sustainable precision irrigation. Full article