The precision fertilization system is the basis for upgrading conventional intensive agricultural production, while achieving both high and quality yields and minimizing the negative impacts on the ...environment. This research aims to present the application of both conventional and modern prediction methods in precision fertilization by integrating agronomic components with the spatial component of interpolation and machine learning. While conventional methods were a cornerstone of soil prediction in the past decades, new challenges to process larger and more complex data have reduced their viability in the present. Their disadvantages of lower prediction accuracy, lack of robustness regarding the properties of input soil sample values and requirements for extensive cost- and time-expensive soil sampling were addressed. Specific conventional (ordinary kriging, inverse distance weighted) and modern machine learning methods (random forest, support vector machine, artificial neural networks, decision trees) were evaluated according to their popularity in relevant studies indexed in the Web of Science Core Collection over the past decade. As a shift towards increased prediction accuracy and computational efficiency, an overview of state-of-the-art remote sensing methods for improving precise fertilization was completed, with the accent on open-data and global satellite missions. State-of-the-art remote sensing techniques allowed hybrid interpolation to predict the sampled data supported by remote sensing data such as high-resolution multispectral, thermal and radar satellite or unmanned aerial vehicle (UAV)-based imagery in the analyzed studies. The representative overview of conventional and modern approaches to precision fertilization was performed based on 121 samples with phosphorous pentoxide (P2O5) and potassium oxide (K2O) in a common agricultural parcel in Croatia. It visually and quantitatively confirmed the superior prediction accuracy and retained local heterogeneity of the modern approach. The research concludes that remote sensing data and methods have a significant role in improving fertilization in precision agriculture today and will be increasingly important in the future.
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•Soil C:N is an indicator of soil quality for sustainable organic farming.•K-means algorithm was implemented for the determination of soil C:N deficiency zones.•Ordinary kriging ...outperformed lognormal kriging and inverse distance weighted.•A total of 45.6% of the study area was determined as unsuitable for organic farming.
Soil carbon-to-nitrogen ratio (C:N) represents an indicator of soil quality and fertility, having a major impact on agricultural land management for organic farming. Determination of soil C:N suitability zones is a necessary procedure in the process, enabling effective land management. A total of 72 soil samples at two soil layers at 0–10 cm and 20–30 cm were used in the study, evenly distributed in the Osijek-Baranja County in eastern Croatia. Ordinary kriging (OK) and Lognormal kriging (LK) using linear, Gaussian and spherical mathematical models, alongside Inverse distance weighted (IDW) were evaluated for the spatial prediction of soil C:N. Inner accuracy representing retention of input sample values in the interpolation results and outer accuracy representing a prediction accuracy at unknown locations were used to determine optimal interpolation parameters. K-means unsupervised classification algorithm was used for the objective determination of soil C:N suitability zones in five classes specified by the Food and Agriculture Organization (FAO). IDW resulted in the highest inner accuracy, while OK with the Gaussian model produced the highest outer accuracy with the average R2 = 0.7908 and NRMSE = 0.0544. Contrary to the previous research, higher mean soil C:N results in a lower soil layer of 20–30 cm with 13.41, compared to 12.03 at 0–10 cm soil layer. The highest soil C:N suitability was determined in only 4.8% of the study area, with the suitability index of 18.44. Meanwhile, the two largest classes were marginally suitable and currently unsuitable class, covering 35.5% and 27.7% of the study area, respectively. These results indicated a necessity for the adjustment of agricultural land management practices to enable sustainable organic farming.
Soybean is regarded as one of the most produced crops in the world, presenting a source of high-quality protein for human and animal diets. The general objective of the study was to determine the ...optimal soybean land suitability and conduct its mapping based on the multicriteria analysis. The multicriteria analysis was based on Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) integration, using Sentinel-2 multitemporal images for suitability validation. The study area covered Osijek-Baranja County, a 4155 km2 area located in eastern Croatia. Three criteria standardization methods (fuzzy, stepwise and linear) were evaluated for soybean land suitability calculation. The delineation of soybean land suitability classes was performed by k-means unsupervised classification. An independent accuracy assessment of calculated suitability values was performed by a novel approach with peak Normalized Difference Vegetation Index (NDVI) values, derived from four Sentinel-2 multispectral satellite images. Fuzzy standardization with the combination of soil and climate criteria produced the most accurate suitability values, having the top coefficient of determination of 0.8438. A total of 14.5% of the study area (602 km2) was determined as the most suitable class for soybean cultivation based on k-means classification results, while 64.3% resulted in some degree of suitability.
Vegetation indices provide information for various precision-agriculture practices, by providing quantitative data about crop growth and health. To provide a concise and up-to-date review of ...vegetation indices in precision agriculture, this study focused on the major vegetation indices with the criterion of their frequency in scientific papers indexed in the Web of Science Core Collection (WoSCC) since 2000. Based on the scientific papers with the topic of “precision agriculture” combined with “vegetation index”, this study found that the United States and China are global leaders in total precision-agriculture research and the application of vegetation indices, while the analysis adjusted for the country area showed much more homogenous global development of vegetation indices in precision agriculture. Among these studies, vegetation indices based on the multispectral sensor are much more frequently adopted in scientific studies than their low-cost alternatives based on the RGB sensor. The normalized difference vegetation index (NDVI) was determined as the dominant vegetation index, with a total of 2200 studies since the year 2000. With the existence of vegetation indices that improved the shortcomings of NDVI, such as enhanced vegetation index (EVI) and soil-adjusted vegetation index (SAVI), this study recognized their potential for enabling superior results to those of NDVI in future studies.
The increasing global demand for food has forced farmers to produce higher crop yields in order to keep up with population growth, while maintaining sustainable production for the environment. As ...knowledge about natural cropland suitability is mandatory to achieve this, the aim of this paper is to provide a review of methods for suitability prediction according to abiotic environmental criteria. The conventional method for calculating cropland suitability in previous studies was a geographic information system (GIS)-based multicriteria analysis, dominantly in combination with the analytic hierarchy process (AHP). Although this is a flexible and widely accepted method, it has significant fundamental drawbacks, such as a lack of accuracy assessment, high subjectivity, computational inefficiency, and an unsystematic approach to selecting environmental criteria. To improve these drawbacks, methods for determining cropland suitability based on machine learning have been developed in recent studies. These novel methods contribute to an important paradigm shift when determining cropland suitability, being objective, automated, computationally efficient, and viable for widespread global use due to the availability of open data sources on a global scale. Nevertheless, both approaches produce invaluable complimentary benefits to cropland management planning, with novel methods being more appropriate for major crops and conventional methods more appropriate for less frequent crops.
The application of global open data remote sensing satellite missions in land monitoring and conservation studies is in the state of rapid growth, ensuring an observation with high spatial and ...spectral resolution over large areas. The purpose of this study was to provide a review of the most important global open data remote sensing satellite missions, current state-of-the-art processing methods and applications in land monitoring and conservation studies. Multispectral (Landsat, Sentinel-2, and MODIS), radar (Sentinel-1), and digital elevation model missions (SRTM, ASTER) were analyzed, as the most often used global open data satellite missions, according to the number of scientific research articles published in Web of Science database. Processing methods of these missions’ data consisting of image preprocessing, spectral indices, image classification methods, and modelling of terrain topographic parameters were analyzed and demonstrated. Possibilities of their application in land cover, land suitability, vegetation monitoring, and natural disaster management were evaluated, having high potential in broad use worldwide. Availability of free and complementary satellite missions, as well as the open-source software, ensures the basis of effective and sustainable land use management, with the prerequisite of the more extensive knowledge and expertise gathering at a global scale.
Global Navigation Satellite Systems (GNSS) in precision agriculture (PA) represent a cornerstone for field mapping, machinery guidance, and variable rate technology. However, recent improvements in ...GNSS components (GPS, GLONASS, Galileo, and BeiDou) and novel remote sensing and computer processing-based solutions in PA have not been comprehensively analyzed in scientific reviews. Therefore, this study aims to explore novelties in GNSS components with an interest in PA based on the analysis of scientific papers indexed in the Web of Science Core Collection (WoSCC). The novel solutions in PA using GNSS were determined and ranked based on the citation topic micro criteria in the WoSCC. The most represented citation topics micro based on remote sensing were “NDVI”, “LiDAR”, “Harvesting robot”, and “Unmanned aerial vehicles” while the computer processing-based novelties included “Geostatistics”, “Precise point positioning”, “Simultaneous localization and mapping”, “Internet of things”, and “Deep learning”. Precise point positioning, simultaneous localization and mapping, and geostatistics were the topics that most directly relied on GNSS in 93.6%, 60.0%, and 44.7% of the studies indexed in the WoSCC, respectively. Meanwhile, harvesting robot research has grown rapidly in the past few years and includes several state-of-the-art sensors, which can be expected to improve further in the near future.
In this study, we propose integrating unmanned aerial systems (UASs) and machine learning classification for suitability prediction of expanding habitats for endangered flora species to prevent ...further extinction. Remote sensing imaging of the protected steppe-like grassland in Bilje using the DJI P4 Multispectral UAS ensured non-invasive data collection. A total of 129 individual flora units of five endangered flora species, including small pasque flower (Pulsatilla pratensis (L.) Miller ssp. nigricans (Störck) Zämelis), green-winged orchid (Orchis morio (L.)), Hungarian false leopardbane (Doronicum hungaricum Rchb.f.), bloody cranesbill (Geranium sanguineum (L.)) and Hungarian iris (Iris variegate (L.)) were detected and georeferenced. Habitat suitability in the projected area, designated for the expansion of the current area of steppe-like grassland in Bilje, was predicted using the binomial machine learning classification algorithm based on three groups of environmental abiotic criteria: vegetation, soil, and topography. Four machine learning classification methods were evaluated: random forest, XGBoost, neural network, and generalized linear model. The random forest method outperformed the other classification methods for all five flora species and achieved the highest receiver operating characteristic (ROC) values, ranging from 0.809 to 0.999. Soil compaction was the least favorable criterion for the habitat suitability of all five flora species, indicating the need to perform soil tillage operations to potentially enable the expansion of their coverage in the projected area. However, potential habitat suitability was detected for the critically endangered flora species of Hungarian false leopardbane, indicating its habitat-related potential for expanding and preventing further extinction. In addition to the current methods of predicting current coverage and population count of endangered species using UASs, the proposed method could serve as a basis for decision making in nature conservation and land management.
This study employed an ensemble machine learning approach to evaluate the effect of bioclimatic covariates on the prediction accuracy of soil total carbon (TC) in the Pannonian biogeoregion. The ...analysis involved two main segments: (1) evaluation of base environmental covariates, including surface reflectance, phenology, and derived covariates, compared to the addition of bioclimatic covariates; and (2) assessment of three individual machine learning methods, including random forest (RF), extreme gradient boosting (XGB), and support vector machine (SVM), as well as their ensemble for soil TC prediction. Among the evaluated machine learning methods, the ensemble approach resulted in the highest prediction accuracy overall, outperforming the individual models. The ensemble method with bioclimatic covariates achieved an R2 of 0.580 and an RMSE of 10.392, demonstrating its effectiveness in capturing complex relationships among environmental covariates. The results of this study suggest that the ensemble model consistently outperforms individual machine learning methods (RF, XGB, and SVM), and adding bioclimatic covariates improves the predictive performance of all methods. The study highlights the importance of integrating bioclimatic covariates when modeling environmental covariates and demonstrates the benefits of ensemble machine learning for the geospatial prediction of soil TC.
Leaf Soil-Plant Analysis Development (SPAD) prediction is a crucial measure of plant health and is essential for optimizing indoor plant management. The deep learning methods offer advanced tools for ...precise evaluations but their adaptation to the heterogeneous indoor plant ecosystem presents distinct challenges. This study assesses how accurately deep neural network (DNN) predicts SPAD values in leaves on indoor plants when compared to well-established machine learning techniques, including Random Forest (RF) and Extreme Gradient Boosting (XGB). The covariates for prediction were based on low-cost multispectral and soil electro-conductivity (EC) sensors, enabling a non-destructive sensing approach. The study also strongly emphasized multicollinearity analysis quantified by the Variance Inflation Factor (VIF) and two independent indices, as well as its effect on prediction accuracy using deep and machine learning methods. DNN resulted in higher accuracy to RF and XGB, also performing better using filtered data after multicollinearity analysis based on the coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) (R2 = 0.589, RMSE = 11.68, MAE = 9.52) in comparison to using all input covariates (R2 = 0.476, RMSE = 12.90, MAE = 10.94). Overall, DNN was proven as a more accurate prediction method than the conventional machine learning approach for the prediction of leaf SPAD values in indoor plants, despite using heterogenous plant types and input covariates.
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