A Comparison of Precise Fertilization Prescription Rates to a Conventional Approach Based on the Open Source GIS Software

Jurišić, Mladen; Radočaj, Dorijan; Plaščak, Ivan; Rapčan, Irena

doi:10.18047/poljo.27.1.7

Cited by 7 publications

(9 citation statements)

References 10 publications

(15 reference statements)

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“…After calculating the present state of a certain soil nutrient, areas of similar values are classified into several zones, with the aim of effective fertilizer application using agricultural machinery which supports precision fertilization [10]. Based on the current zones of nutrient status in the soil, the application zones for fertilization is calculated by subtracting the existing values from those prescribed by the agricultural expert [4]. Prescription maps are used to manage agricultural machinery as the final product of the whole process of computer processing.…”

Section: Integration Of Geoinformation Technologies With Agronomic Pr...mentioning

confidence: 99%

“…Modern intensive agriculture makes fertilization one of the primary ways to achieve this goal; however, the short-term objectives of agricultural land management could seriously affect the long-term sustainability of food production [2]. Several authors have pointed out that farmers should not only consider fertilization with the aim of increasing their economic profits, but also in relation to maintaining the long-term biochemical soil composition and its effects on the ecosystem [3,4]. Poor management and understanding of fertilization itself have a significant negative impact on the environment, atmosphere, and natural resources, in addition to poor economic impacts on agricultural production [5].…”

Section: Introductionmentioning

confidence: 99%

“…Instead, precision agriculture considers the agricultural parcel as a heterogeneous system, which is often very variable in its local spatial units [8]. Such an approach aims to balance the nutrients in the soil on the entire agricultural plot, so that all its parts enable optimal crop development [4]. Fertilization of agricultural crops is regularly performed in several iterations, following the current crop growth stage and the implementation of agrotechnical operations [9].…”

Section: Introductionmentioning

confidence: 99%

“…Papadopoulus et al [13] stated that the key component in site-specific crop cultivation is geoinformation technologies, while agricultural land management depends on ecological and natural resources that have a spatial component. Geoinformation technologies are becoming a key factor in precision fertilization, as a basis for integrating discrete spatial data of soil sampling and their spatial prediction [4]. In addition to the direct application of spatial interpolation methods in precision fertilization, there is a successful long-term application in related soil-related activities.…”

Section: Introductionmentioning

confidence: 99%

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The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture

2022

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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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Section: Integration Of Geoinformation Technologies With Agronomic Pr...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture

2022

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“…Furthermore, they enable the obtainment of data with a high temporal resolution, which is extremely important for an early detection of crop diseases. Monitoring the crop condition in the early phenological stages may indicate the possible issues during crop development (Jurišić et al, 2021). Precision agriculture retrieves the data collected by remote sensing from various sources, thus empowering a farmer to make decisions about the necessary agrotechnical operations in the shortest possible time (Candiago et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Analiza rgb i multispektralne kamere na bespilotnome zrakoplovu za klasifikaciju kukuruza strojnim učenjem

Jurišić

Radočaj

Plaščak

et al. 2022

Poljoprivreda (Online)

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This study investigated a crop and soil classification applying the Random Forest machine learning algorithm based on the red-green-blue (RGB) and multispectral sensor imaging deploying an unmanned aerial vehicle (UAV). The study area covered two 10 x 10 m subsets of a maize-sown agricultural parcel near Koška. The highest overall accuracy was obtained in the combination of the red edge (RE), near-infrared (NIR), and normalized difference vegetation index (NDVI) in both subsets, with a 99.8% and 91.8% overall accuracy, respectively. The conducted analysis proved that the RGB camera obtained sufficient accuracy and was an acceptable solution to the soil and vegetation classification. Additionally, a multispectral camera and spectral analysis allowed for a more detailed analysis, primarily of the spectrally similar areas. Thus, this procedure represents a basis for both the crop density calculation and weed detection while deploying an unmanned aerial vehicle. To ensure crop classification effectiveness in practical application, it is necessary to further integrate the weed classes in the current vegetation class and separate them into crop and weed classes.

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Evaluation of Ensemble Machine Learning for Geospatial Prediction of Soil Iron in Croatia

Radočaj,

Tuno,

Mulahusić

et al. 2023

Poljoprivreda

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Soil fertility is pivotal for agricultural productivity, and iron (Fe) is a critical micro- nutrient essential for a successful crop development. This study investigates a potential of ensemble machine-learning methods in geospatial prediction of soil Fe in Croatia. Using a dataset of 686 soil samples, three individual machine-learning methods, including the extreme gradient boosting (XGB), support vector machine (SVM), and Cubist, as well as their ensemble, were evaluated for the soil Fe predi- ction. The ensemble method outperformed the individual models, exhibiting a higher prediction accuracy expressed by the coefficient of determination (R2 = 0.578), with a lower root-mean-square error (RMSE = 0.837) and the mean absolute error (MAE = 0.550). The soil clay content emerged as the most influential predictor, followed by the sand content, pH values, and select bioclimatic variables. This study’s results demonstrate the effectiveness of ensemble machine learning in an accurate predicti- on of soil Fe content and contribute to an informed decision-making in sustainable agricultural land-use planning and management. By including the complementary machine-learning methods into an ensemble with the representative environmental covariates, a geospatial prediction aids to a reliable comprehension of soil proper- ties and their spatial variability.

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A Comparison of Precise Fertilization Prescription Rates to a Conventional Approach Based on the Open Source GIS Software

Cited by 7 publications

References 10 publications

The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture

The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture

Analiza rgb i multispektralne kamere na bespilotnome zrakoplovu za klasifikaciju kukuruza strojnim učenjem

Evaluation of Ensemble Machine Learning for Geospatial Prediction of Soil Iron in Croatia

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