Bálint Daróczy

36951699400

Publications - 7

Predicting maize growth and biomass: Integrating gradient boosted trees with sentinel images and IoT

Bálint Daróczy Bálint Ambrus Gergely Teschner Tarek Alahmad A. Nyéki M. Neményi

Publication Name: Progress in Agricultural Engineering Sciences

Publication Date: 2025-01-01

Volume: Unknown

Issue: Unknown

Page Range: Unknown

Description:

Agricultural big data and high-performance computing have significantly improved crop yield modeling. Maize growth dynamics and yield prediction are crucial for sustainable agriculture. This study introduces an advanced modeling approach utilizing Gradient Boosted Decision Trees (GBDT) combined with a feature selection strategy to predict maize biomass production. A dataset of 200 unique maize plants was observed throughout the vegetation season. Our approach integrates manual measurements, meteorological data, and vegetation indices along with Internet of Things (IoT) field sensors to perform spatio-temporal analysis. Results indicate that maize stalk thickness and height are the most reliable predictors of biomass yield, while environmental variables show minimal impact. The most effective model, period-dependent GBDT, demonstrated superior predictive performance, achieving an average error of 4.39 mm in plant growth predictions. Notably, stalk thickness and height can be estimated six weeks before harvest, while biomass yield two weeks before harvest. This research underscores the potential of machine learning and remote sensing to enhance precision agriculture decision-making.

Open Access: Yes

DOI: 10.1556/446.2025.00202

Spatial Variability of Soil Properties and Its Effect on Maize Yields within Field—A Case Study in Hungary

Bálint Daróczy C. Kerepesi A. J. Kovács A. Nyéki M. Neményi

Publication Name: Agronomy

Publication Date: 2022-02-01

Volume: 12

Issue: 2

Page Range: Unknown

Description:

To better understand the potential of soils, understanding how soil properties vary over time and in-field is essential to optimize the cultivation and site-specific technologies in crop pro-duction. This article aimed at determining the within-field mapping of soil chemical and physical properties, vegetation index, and yield of maize in 2002, 2006, 2010, 2013, and 2017, respectively. The objectives of this five-year field study were: (i) to assess the spatial and temporal variability of attributes related to the maize yield; and (ii) to analyse the temporal stability of management zones. The experiment was carried out in a 15.3 ha research field in Hungary. The soil measurements in-cluded sand, silt, clay content (%), pH, phosphorous (P2O5), potassium (K2O), and zinc (Zn) in the topsoil (30 cm). The apparent soil electrical conductivity was measured in two layers (0–30 cm and 30–90 cm, mS/m) in 2010, in 2013, and in 2017. The soil properties and maize yields were evaluated in 62 management zones, covering the whole research area. The properties were characterized as the spatial-temporal variability of these parameters and crop yields. Classic statistics and geostatis-tics were used to analyze the results. The maize yields were significantly positively correlated (r = 0.62–0.73) with the apparent electrical conductivity (Veris_N3, Veris_N4) in 2013 and 2017, and with clay content (r = 0.56–0.81) in 2002, 2013, and 2017.

Open Access: Yes

DOI: 10.3390/agronomy12020395

Application of spatio-temporal data in site-specific maize yield prediction with machine learning methods

E. Harsányi G. Milics Bálint Daróczy C. Kerepesi A. J. Kovács J. Nagy A. Nyéki M. Neményi András Benczúr

Publication Name: Precision Agriculture

Publication Date: 2021-10-01

Volume: 22

Issue: 5

Page Range: 1397-1415

Description:

In order to meet the requirements of sustainability and to determine yield drivers and limiting factors, it is now more likely that traditional yield modelling will be carried out using artificial intelligence (AI). The aim of this study was to predict maize yields using AI that uses spatio-temporal training data. The paper has advanced a new method of maize yield prediction, which is based on spatio-temporal data mining. To find the best solution, various models were used: counter-propagation artificial neural networks (CP-ANNs), XY-fused Querynetworks (XY-Fs), supervised Kohonen networks (SKNs), neural networks with Rectangular Linear Activations (ReLU), extreme gradient boosting (XGBoost), support-vector machine (SVM), and different subsets of the independent variables in five vegetation periods. Input variables for modelling included: soil parameters (pH, P2O5, K2O, Zn, clay content, ECa, draught force, Cone index), micro-relief averages, and meteorological parameters for the 63 treatment units in a 15.3 ha research field. The best performing method (XGBoost) reached 92.1% and 95.3% accuracy on the training and the test sets. Additionally, a novel method was introduced to treat individual units in a lattice system. The lattice-based smoothing performed an additional increase in Area under the curve (AUC) to 97.5% over the individual predictions of the XGBoost model. The models were developed using 48 different subsets of variables to determine which variables consistently contributed to prediction accuracy. By comparing the resulting models, it was shown that the best regression model was Extreme Gradient Boosting Trees, with 92.1% accuracy (on the training set). In addition, the method calculates the influence of the spatial distribution of site-specific soil fertility on maize grain yields. This paper provides a new method of spatio-temporal data analyses, taking the most important influencing factors on maize yields into account.

Open Access: Yes

DOI: 10.1007/s11119-021-09833-8

SZTAKI @ ImageCLEFmed 2020 Tuberculosis Task

Bálint Daróczy Bence Lestyan András Benczúr

Publication Name: Ceur Workshop Proceedings

Publication Date: 2020-01-01

Volume: 2696

Issue: Unknown

Page Range: Unknown

Description:

In this paper we describe our submission to the ImageCLEFmed 2020 Tuberculosis task and discuss additional results on the training set with various neural networks. After some centralization and normalization we independently categorized the 2D slices with convolutional neural networks (traditional and residual feed-forward networks) and we aggregated the individual predictions based on the positions of the lung and the slices. Our additional experiments with various aggregation methods indicate that individual slices do not necessary contain enough information about such complex structures.

Open Access: Yes

DOI: DOI not available

Session recommendation via recurrent neural networks over fisher embedding vectors

Bálint Daróczy Frederick Ayala-Gómez Domokos Kelen Anna Ország András Benczúr

Publication Name: Sensors Switzerland

Publication Date: 2019-08-02

Volume: 19

Issue: 16

Page Range: Unknown

Description:

Recommendation services bear great importance in e-commerce, shopping, tourism, and social media, as they aid the user in navigating through the items that are most relevant to their needs. In order to build recommender systems, organizations log the item consumption in their user sessions by using different sensors. For instance, Web sites use Web data loggers, museums and shopping centers rely on user in-door positioning systems to register user movement, and Location-Based Social Networks use Global Positioning System for out-door user tracking. Most organizations do not have a detailed history of previous activities or purchases by the user. Hence, in most cases recommenders propose items that are similar to the most recent ones viewed in the current user session. The corresponding task is called session based, and when only the last item is considered, it is referred to as item-to-item recommendation. A natural way of building next-item recommendations relies on item-to-item similarities and item-to-item transitions in the form of “people who viewed this, also viewed” lists. Such methods, however, depend on local information for the given item pairs, which can result in unstable results for items with short transaction history, especially in connection with the cold-start items that recently appeared and had no time yet to accumulate a sufficient number of transactions. In this paper, we give new algorithms by defining a global probabilistic similarity model of all the items based on Random Fields. We give a generative model for the item interactions based on arbitrary distance measures over the items, including explicit, implicit ratings and external metadata to estimate and predict item-to-item transition probabilities. We exploit our new model in two different item similarity algorithms, as well as a feature representation in a recurrent neural network based recommender. Our experiments on various publicly available data sets show that our new model outperforms simple similarity baseline methods and combines well with recent item-to-item and deep learning recommenders under several different performance metrics.

Open Access: Yes

DOI: 10.3390/s19163498

Maize yield prediction based on artificial intelligence using spatio-temporal data

G. Milics Bálint Daróczy C. Kerepesi A. J. Kovács A. Nyéki M. Neményi András Benczúr

Publication Name: Precision Agriculture 2019 Papers Presented at the 12th European Conference on Precision Agriculture Ecpa 2019

Publication Date: 2019-01-01

Volume: Unknown

Issue: Unknown

Page Range: 1011-1017

Description:

The aim of this study was to predict maize yield by artificial intelligence using spatio-temporal training data. Counter-propagation artificial neural networks (CP-ANNs), XY-fused networks (XY-Fs), supervised Kohonen networks (SKNs), extreme gradient boosting (XGBoost) and support-vector machine (SVM) were used for predicting maize yield in 5 vegetation periods. Input variables for modelling were: soil parameters (pH, P2O5, K2O, Zn, Clay content, ECa, draught force, Cone index), and micro-relief averages and meteorological parameters for the 63 treatment units. The best performing method (XGBoost) attained 92.1 and 95.3% of accuracy on the training and the test set.

Open Access: Yes

DOI: 10.3920/978-90-8686-888-9_124

The applicability of on-line contextual calibration to a neural network based monocular collision avoidance system on a UAV

Bálint Daróczy Antal Hiba Koppány Pázmán Rita Aleksziev Péter Bauer András Benczúr Ákos Zarándy

Publication Name: IFAC Papersonline

Publication Date: 2019-01-01

Volume: 52

Issue: 11

Page Range: 7-12

Description:

Contextual calibration for object detection is a technique where a pretrained network collects attractive false positives during a calibration phase and use this calibration data for further training. This paper investigates the applicability of this method to a vision based onboard sense and avoid system, which requires intruder aircraft detection in camera images. Various landscape and sky backgrounds were generated by Unreal4 3D engine for calibration tests. Contextual calibration is a promising candidate for handling extreme situations which are not covered well in the training data.

Open Access: Yes

DOI: 10.1016/j.ifacol.2019.09.110