Precision Agriculture

Abstract Olive groves, often characterized by complex topography and highly variable soils, present challenges for delineating irrigation management zones (MZs). This study addresses this issue by examining the relevance of apparent electrical conductivity (ECa), elevation (Z), topographic wetness index (TWI) and time-series of Sentinel-2 NDVI imagery for delimiting MZs for variable rate irrigation (VRI) in a 40-ha olive grove in southern Spain. Principal Component Analysis (PCA) was employed to disentangle olive and grass cover NDVI patterns. PC1 represented the olive tree development patten and showed little relationship with soil properties, while PC2 was associated with the grass cover growth pattern and considered a proxy for water storage-related soil properties that are relevant for irrigation scheduling. An alternative analysis using NDVI percentiles yielded similar results but favored PCA for distinguishing between grass cover and olive tree development patterns. Correlation between NDVI and ECa varied seasonally (r > 0.60), driven by the grass cover dynamics. To assess also possible non-linear relationships, regression trees were used to estimate NDVI percentiles, emphasizing the importance of ECa, ECaratio, Z, and slope in predicting different NDVI percentiles. Fuzzy k-means zoning using ECa + Z resulted in four classes that best classified variables that are relevant for irrigation scheduling due to their relationship with soil water storage (e.g. clay content, P0.95 and PC2). Zonings based on ECa, ECa + Z + TWI and ECa + Z + TWI + NDVI yielded two zones that classified P0.95 and PC2 well, but not clay content. Therefore, the zoning based on ECa + Z was chosen as optimal in the context of this VRI applications. Our analysis showed how NDVI series can be used in combination with ECa and elevation to evaluate the effectiveness of different zoning approaches for developing VRI prescriptions in olive groves.

Abstract Purpose The use of Unmanned aerial vehicle (UAV) data for predicting crop above-ground biomass (AGB) is becoming a more feasible alternative to destructive methods. However, canopy height, vegetation index (VI), and other traditional features can become saturated during the mid to late stages of crop growth, significantly impacting the accuracy of AGB prediction. Methods In 2022 and 2023, UAV multispectral, RGB, and light detection and ranging point cloud data of wheat populations were collected at seven growth stages across two experimental fields. The point cloud depth features were extracted using the improved PointNet++ network, and AGB was predicted by fusion with VI, color index (CI), and texture index (TI) raster image features. Results The findings indicate that when the point cloud depth features were fused, the R2 values predicted from VI, CI, TI, and canopy height model images increased by 0.05, 0.08, 0.06, and 0.07, respectively. For the combination of VI, CI, and TI, R2 increased from 0.86 to a maximum of 0.9, while the root-mean-square error (RMSE) and mean absolute error were 1.80 t ha−1 and 1.36 t ha−1, respectively. Additionally, our findings revealed that the hybrid fusion exhibits the highest accuracy, it demonstrates robust adaptability in predicting AGB across various years, growth stages, crop varieties, nitrogen fertilizer applications, and densities. Conclusion This study effectively addresses the saturation in spectral and chemical information, provides valuable insights for high-precision phenotyping and advanced crop field management, and serves as a reference for studying other crops and phenotypic parameters.

Abstract In the context of limited resources and a growing demand for food due to an increase in the worldwide population, irrigation plays a vital role, and the efficient use of water is a major objective. In pressurized irrigation systems, water management is linked to high energy requirements, which is especially relevant in sprinkler irrigation. Therefore, decision support models are important for optimizing the design and management of irrigation systems. In this study, a holistic model for solid set irrigation systems (SORA 2024) was developed. This new model integrates hydraulic models at the subunit and plot levels to evaluate the distribution of pressure (EPANET, Rossman in The EPANET programmer’s toolkit for analysis of water distribution systems, Tempe, Arizona, 1999), the discharge and water distribution for each emitter (SIRIAS, Carrion et al. in , Irrig Sci 20(2):73–84, 2001) and the distribution of water applied by all the emitters of the subunit (SORA, Carrión et al. in Irrig Sci 20(2): 73–84, 2001). The integrated model also includes crop simulation (AQUACROP, Steduto et al. in Agron J 101(3), 426–437, 2009). to assess the effect of water distribution on crop production. The objective of this holistic model is to assist in decision-making processes for designing, sizing, upgrading, and managing solid set irrigation systems at the sprinkler level. The new integrated model (SORA 2024) was applied to a 2.84 ha commercial plot with 2 irrigation sectors that grow onion crops (Allium cepa L.). It was used to analyse each irrigation event from a real irrigation season, considering the conditions (pressure, irrigation time/periods, environmental conditions, and so on). The analysis is based on the sprinkler–nozzle combination, working pressure and wind direction and intensity during each irrigation event. The model also accounts for the cumulative effect/impact of all irrigation events on the plot. The model was validated through field trials using the “crop as a sensor” approach (Sarig et al. in , Agron 11(3):2021). To demonstrate the effectiveness of the model, the choice of nozzles in each sprinkler of the subunit was optimized. This is a quick and cost-effective way for farmers to improve their irrigation systems. By using this method, farmers can achieve better uniformity of water application and a slight increase in crop yield while maintaining the same irrigation schedule and amount of water used. Furthermore, the model enables farmers to work at the emitter level while integrating the results for the entire plot. This allows for precise irrigation of variable dosages by using different sprinkler–nozzle combinations in the same subunit. Farmers can do this based on the prior zoning of the plot, which is determined by its productive potential. This justifies the use of different irrigation dosages in each zone.

Abstract Introduction Satellite-sourced data have become a valuable resource for precision agriculture because they provide crucial insights into various parameters that are essential for effective crop management. An array of practical agricultural tools provides comprehensive data for assessing crop biomass, soil conditions, and plant stress symptoms, predicting yields, and performing other functions. Satellite data, when combined with in situ data from different sources, can significantly enhance biomass and yield estimations. Material and Methods The ability of the “PROcesses of radiation, Mass and Energy Transfer” (PROMET) model to predict crop biomass and grain yield and to optimize nitrogen fertilization during the vegetation period was investigated. Field trials were conducted to assess the accuracy and limitations of biomass and yield predictions. Results and Conclusion The predicted yields were sufficiently accurate on a whole-field basis, and site-specific values showed strong correlations. In additional field trials with different fertilization strategies, the highest yield and nitrogen efficiency were observed for the PROMET-based strategy. Additional experiments with different crops and greater durations are needed to draw a more reliable conclusion.