A. Nyéki
6507906325
Publications - 24
Innovative computer vision methods for tomato (Solanum Lycopersicon) detection and cultivation: a review
Publication Name: Discover Applied Sciences
Publication Date: 2025-09-01
Volume: 7
Issue: 9
Page Range: Unknown
Description:
In recent years, machine vision, deep learning, and artificial intelligence have garnered significant research interest in precision agriculture. This article aims to provide a comprehensive review of the latest advancements in machine vision application in tomato cultivation. This study explores integrating cognitive technologies in agriculture, particularly in tomato production. The review covers various studies on tomatoes and machine vision that support tomato harvesting, such as classification, fruit counting, and yield estimation. It addresses plant health monitoring approaches, including detecting weeds, pests, leaf diseases, and fruit disorders. The paper also examines the latest research efforts in vehicle navigation systems and tomato-harvesting robots. The primary objective of this article was to present a thorough analysis of the image processing algorithms utilized in research over the past two years, along with their outcomes.
Open Access: Yes
Aerial Image-Based Crop Row Detection and Weed Pressure Mapping Method
Publication Name: Agronomy
Publication Date: 2025-08-01
Volume: 15
Issue: 8
Page Range: Unknown
Description:
Accurate crop row detection is crucial for determining weed pressure (weeds item per square meter). However, this task is complicated by the similarity between crops and weeds, the presence of missing plants within rows, and the varying growth stages of both. Our hypothesis was that in drone imagery captured at altitudes of 20–30 m—where individual plant details are not discernible—weed presence among crops can be statistically detected, allowing for the generation of a weed distribution map. This study proposes a computer vision detection method using images captured by unmanned aerial vehicles (UAVs) consisting of six main phases. The method was tested on 208 images. The algorithm performs well under normal conditions; however, when the weed density is too high, it fails to detect the row direction properly and begins processing misleading data. To investigate these cases, 120 artificial datasets were created with varying parameters, and the scenarios were analyzed. It was found that a rate variable—in-row concentration ratio (IRCR)—can be used to determine whether the result is valid (usable) or invalid (to be discarded). The F1 score is a metric combining precision and recall using a harmonic mean, where “1” indicates that precision and recall are equally weighted, i.e., β = 1 in the general Fβ formula. In the case of moderate weed infestation, where 678 crop plants and 600 weeds were present, the algorithm achieved an F1 score of 86.32% in plant classification, even with a 4% row disturbance level. Furthermore, IRCR also indicates the level of weed pressure in the area. The correlation between the ground truth weed-to-crop ratio and the weed/crop classification rate produced by the algorithm is 98–99%. As a result, the algorithm is capable of filtering out heavily infested areas that require full weed control and capable of generating weed density maps on other cases to support precision weed management.
Open Access: Yes
Soil Moisture Content Prediction Using Gradient Boosting Regressor (GBR) Model: Soil-Specific Modeling with Five Depths
Publication Name: Applied Sciences Switzerland
Publication Date: 2025-06-01
Volume: 15
Issue: 11
Page Range: Unknown
Description:
Monitoring soil moisture content (SMC) remains challenging due to its spatial and temporal variability. Accurate SMC prediction is essential for optimizing irrigation and enhancing water use efficiency. In this research, a Gradient Boosting Regressor (GBR) model was developed and validated to predict SMC in two soil textures, loam and silt loam, using meteorological data from Internet of Things (IoT) sensors and gravimetric SMC field measurements collected from five different depths. The statistical analysis revealed significant variation in SMC across depths in loam soil (p < 0.05), while silt loam exhibited more stable moisture distribution. The GBR model demonstrated high performance in both soil textures, achieving R2 values of 0.98 and 0.94 for silt loam and loam soils, respectively, with low prediction errors (RMSE 0.85 and 0.97, respectively). Feature importance analysis showed that precipitation and humidity were the most influential features in loam soil, while solar radiation had the highest impact on prediction in silt loam soil. Soil depth also showed a significant contribution to SMC prediction in both soils. These results highlight the necessity for soil-specific modeling to enhance SMC prediction accuracy, optimize irrigation systems, and support water resources management approaches aligning with SDG6 objectives.
Open Access: Yes
DOI: 10.3390/app15115889
Predicting maize growth and biomass: Integrating gradient boosted trees with sentinel images and IoT
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
Global challenges and the ‘farm to fork’ strategies of the European Green Deal: Blessing or curse
Publication Name: Progress in Agricultural Engineering Sciences
Publication Date: 2024-12-12
Volume: 20
Issue: 1
Page Range: 101-111
Description:
The article evaluates how well the goals of the European Green Deal are justified, especially considering the risks to energy and food security arising from the conflict between Russia and Ukraine. We agree with the objectives of the European Green Agreement as a whole, but whether some of the objectives which feature in the EASAC study can be achieved by 2030 is questionable, and the description of the tools necessary to achieve the objectives is incomplete. Among other things, there is hardly any mention of the role played by precision farming with digitalization, which is a revolutionary change from an ecological and economic point of view, in reducing the use of synthetic inputs, in regenerating the original state of the soil, in reducing GHG emissions, thus in increasing biodiversity, and at the same time in intensifying production, and finally in expanding the application of biotechnology. We examine these areas in our analysis. Some of the objectives of the EASAC study to be achieved by 2030 are subject to debate, and the description of the information and communication conditions necessary to achieve the objectives is incomplete. The IoT (Internet of Things) responds to global and local challenges: it integrates the precision technologies, WSNs (Wireless Sensor Networks), artificial intelligence, mobile field (Smart Small Robots) and remote data loggers (UAVs: Unmanned Air Vehicles and satellites), Big Data, and cloud computing. Consequently, decision support is increasingly developing into unmanned decision making. IoT (Internet of Things) is the basis of “Farm to Fork” and “Lab to Field” monitoring approaches. This article evaluates the implementation of European Green Agreement objectives in light of energy and food security risks arising from the Russia-Ukraine conflict. While overall support for the agreement exists, the feasibility of certain EASAC study objectives by 2030 is called into question due to insufficient tools specifications. Notably absent is the emphasis on precision farming with digitalization, which is a transformative ecological and economic practice. Our analyses look into its function in reducing synthetic inputs, soil regeneration, GHG emission reduction, biodiversity enhancement, production intensification, and biotechnology development. Debates surround EASAC study objectives for 2030, despite limited information and communication restrictions. The Internet of Things (IoT) arises as a solution, combining precision technology, WSNs (wireless sensor networks), AI (artificial intelligence), smart small robots, UAVs (unmanned aerial vehicles), satellites, big data, and cloud computing. As a result, decision support turns toward unmanned decision-making, with IoT laying the groundwork for “Farm to Fork” and “Lab to Field” monitoring systems.
Open Access: Yes
The Effect of Ascorbic Acid on Salt Tolerance and Seedling Performance in Triticum durum Defs. ‘Douma 3’ Under Salinity Stress in Syria
Publication Name: Agronomy
Publication Date: 2024-12-01
Volume: 14
Issue: 12
Page Range: Unknown
Description:
This study was conducted to evaluate the laboratory tolerance of the durum wheat cultivar (Douma 3) when treated with two levels of ascorbic acid (5 ppm and 10 ppm, in addition to a control treatment soaked in water) under two levels of salt stress (50 mM and 100 mM NaCl, in addition to a control). The experiment took place at the Field Crops Department labs, Faculty of Agricultural Engineering, University of Damascus, during the 2022–2023 agricultural season. The aim was to study the effect of ascorbic acid on seed reserve utilization efficiency, peroxidase enzyme activity, and its role in salt stress tolerance. The experiment followed a randomized complete block design (RCBD) using factorial ANOVA with two replicates. The results showed significant differences between the treatments, with the priming of seeds soaked in a 5 ppm ascorbic acid solution (A1) significantly outperforming in terms of seedling dry weight (22.67 mg/seedling), remaining seed dry weight (7.5 mg/seed), seed reserve utilization efficiency (0.47 mg/mg), and salt tolerance index (89.80%). Simple correlation analysis showed a significant positive correlation between seedling dry weight (SDW), seed reserve utilization efficiency (SRUE) (0.881), and salt tolerance index (STI) (0.746 *). However, a negative and non-significant relationship was observed between the remaining seed dry weight (RSDW) and other traits. Moreover, SRUE had a significant positive correlation with STI (0.814). Both total soluble protein concentrations and peroxidase enzyme activity increased under salt stress conditions following pre-treatment with ascorbic acid compared to the control. The highest protein concentration and peroxidase enzyme activity were observed with the 5 ppm ascorbic acid treatment (A1).
Open Access: Yes
Field-grown tomato yield estimation using point cloud segmentation with 3D shaping and RGB pictures from a field robot and digital single lens reflex cameras
Publication Name: Heliyon
Publication Date: 2024-10-30
Volume: 10
Issue: 20
Page Range: Unknown
Description:
The aim of this study was to estimate field-grown tomato yield (weight) and quantity of tomatoes using a self-developed robot and digital single lens reflex (DSLR) camera pictures. The authors suggest a new approach to predicting tomato yield that is based on images taken in the field, 3D scanning, and shape. Field pictures were used for tomato segmentation to determine the ripeness of the crop. A convolution neural network (CNN) model using TensorFlow library was devised for the segmentation of tomato berries along with a small robot, which had a 59.3 % F1 score. To enhance the accurate tomato crop model and to estimate the yield later, point cloud imaging was applied using a Ciclops 3D scanner. The best fitting sphere model was generated using the 3D model. The most optimal model was the 3D model, which gave the best representation and provided the weight of the tomatoes with a relative error of 21.90 % and a standard deviation of 17.9665 %. The results indicate a consistent object-based classification of the tomato crop above the plant/row level with an accuracy of 55.33 %, which is better than in-row sampling (images taken by the robot). By comparing the measured and estimated yield, the average difference for DSLR camera images was more favorable at 3.42 kg.
Open Access: Yes
Weed detection in agricultural fields using machine vision
Publication Name: Bio Web of Conferences
Publication Date: 2024-08-23
Volume: 125
Issue: Unknown
Page Range: Unknown
Description:
Weeds have the potential to cause significant damage to agricultural fields, so the development of weed detection and automatic weed control in these areas is very important. Weed detection based on RGB images allows more efficient management of crop fields, reducing production costs and increasing yields. Conventional weed control methods can often be time-consuming and costly. It can also cause environmental damage through overuse of chemicals. Automated weed detection and control technologies enable precision agriculture, where weeds are accurately identified and targeted, minimizing chemical use and environmental impact. Overall, weed detection and automated weed control represent a significant step forward in agriculture, helping farmers to reduce production costs, increase crop safety, and develop more sustainable agricultural practices. Thanks to technological advances, we can expect more efficient and environmentally friendly solutions for weed control in the future. Developing weed detection and automated control technologies is crucial for enhancing agricultural efficiency. Employing RGB images for weed identification not only lowers production costs but also mitigates environmental damage caused by excessive chemical use. This study explores automated weed detection systems, emphasizing their role in precision agriculture, which ensures minimal chemical use while maximizing crop safety and sustainability.
Open Access: Yes
Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
Publication Name: Applied Sciences Switzerland
Publication Date: 2024-06-01
Volume: 14
Issue: 11
Page Range: Unknown
Description:
In agriculture, as precision farming increasingly employs robots to monitor crops, the use of weeding and harvesting robots is expanding the need for computer vision. Currently, most researchers and companies address these computer vision tasks with CNN-based deep learning. This technology requires large datasets of plant and weed images labeled by experts, as well as substantial computational resources. However, traditional feature-based approaches to computer vision can extract meaningful parameters and achieve comparably good classification results with only a tenth of the dataset size. This study presents these methods and seeks to determine the minimum number of training images required to achieve reliable classification. We tested the classification results with 5, 10, 20, 40, 80, and 160 images per weed type in a four-class classification system. We extracted shape features, distance transformation features, color histograms, and texture features. Each type of feature was tested individually and in various combinations to determine the best results. Using six types of classifiers, we achieved a 94.56% recall rate with 160 images per weed. Better results were obtained with more training images and a greater variety of features.
Open Access: Yes
DOI: 10.3390/app14114839
Precision agricultural technology for advanced monitoring of maize yield under different fertilization and irrigation regimes: A case study in Eastern Hungary (Debrecen)
Publication Name: Journal of Agriculture and Food Research
Publication Date: 2024-03-01
Volume: 15
Issue: Unknown
Page Range: Unknown
Description:
Precision agricultural (PrA) technology relies on the utilization of special equipment to access real time observations on plant health status, chlorophyll, nitrogen content, and soil moisture content. In this research new PrA technology (i.e., SPAD (Soil Plant Analysis Development), and UAV-based NDVI (Unmanned Aerial Vehicle-based Normalized Difference Vegetation Index) were used to monitor maize yield based on different filed trials in eastern part of Hungary. Our study aimed to examine the utilization of PrA technology specifically SPAD and UAV-based NDVI measurements for monitoring maize GY under irrigated and rainfed experimental setups in Hungary with varied nitrogen treatment for the year 2022. The results showed that the SPAD increased in all treatments (14.7 %; p < 0.05) from V6–V8 in the rainfed treatments, decreased significantly (p < 0.05) by 13.9 % (R1) and 30.6 % (R3). However, implementation of irrigation significantly increased the SPAD values in majority of treatments. Also, results reveal that, under irrigated and rainfed conditions the highest UAV-based NDVI value (0.703, 0.642) was obtained in V12 (A120 treatment) and highest NDVI value (0.728, 0.662) was obtained in Vn (A120 treatment). Remarekedly, irrigation led to significant differences (p < 0.05) of UAV-based NDVI values compared with none irrigated. On the other hand, implementation of 120 kg N ha−1 before sowing led to highest GY, especially under irrigated conditions (8.649 Mg ha−1). The overall mean GY under rainfed treatment was 6.256 Mg ha−1, while under irrigated treatment it increased by 37.2 % and reached 8.581 Mg ha−1 (p < 0.05). In conclusion, PrA technology will support farmers in making informed decisions regarding fertilization strategies and timing, which will in turn maximize yield and minimize risk.
Open Access: Yes
The Effect of Illumination on HSV Colour Segmentation for Ripe Tomatoes based on Machine Vision
Publication Name: Chemical Engineering Transactions
Publication Date: 2024-01-01
Volume: 114
Issue: Unknown
Page Range: 829-834
Description:
In agriculture, computer vision and image processing are essential for monitoring crops and controlling robots and actuators. In this work, the detection of ripe tomato fruit was the main aim. During the tomato-ripping process, the green tomato turns to red in several color stages (Ambrus et al., 2024). While the chlorophyll concentration decreases, the lycopene concentration increases. The sugar and the acid increase parallel to lycopene. The RGB camera can capture the process but needs to convert HSV color space to identify the tomato. The successful identification depends on the direct illumination volume. The experiment contains 4 ripe tomatoes and 15 different artificial illumination levels. The measurements show that the results are similar to or constantly above 3,000 lx illumination. However, under 3,000 lx, the detected size of tomatoes looks smaller and smaller depending on the weakness of illumination. Around 1,600 lx, it is possible to measure only half of the real size of the tomato. It shows that using the right amount of light is crucial to precise measurement in HSV color space. This research highlights the critical importance of proper illumination in ensuring accurate image analysis for tasks like industrial tomato segmentation. It emphasizes the need for adaptable lighting solutions, particularly in varying weather conditions, and the balance between adequate light and energy efficiency.
Open Access: Yes
DOI: 10.3303/CET24114139
Challenges of ecocentric sustainable development in agriculture with special regard to the internet of things (IoT), an ICT perspective
Publication Name: Progress in Agricultural Engineering Sciences
Publication Date: 2023-12-20
Volume: 19
Issue: 1
Page Range: 113-122
Description:
“Feed the global population and regenerate the planet.” The conditions necessary for the implementation of the above commonly used slogan did not exist 10–15 years ago. We did not have access to the information and databases that would have allowed us to increase yields for the purpose of feeding the growing population. While increasingly meeting sustainability requirements and regenerating the Earth. Anthropocentrism, the belief that humans are superior to everything else, benefits humans by exploiting human greed and ignorance, which is a dead end for both individuals and societies. Only humans can ignore the dynamic equilibrium processes of nature and disregard the consequences that adversely affect future generations. Ecocentric agricultural practices have several prerequisites. It is important for the academic sphere to recognize its significance. Another fundamental challenge is the continuous monitoring of the production unit and its close and distant environment for the purpose of decision preparation using Big Data. The Internet of Things (IoT) is a global infrastructure that represents the network of physical (sensors) and virtual (reality) “things” through interoperable communication protocols. This allows devices to connect and communicate using cloud computing and artificial intelligence, contributing to the integrated optimization of the production system and its environment, considering ecocentric perspectives. This brings us closer to the self-decision-making capability of artificial intelligence, the practice of machine-to-machine (M2M) interaction, where human involvement in decision-making is increasingly marginalized. The IoT enables the fusion of information provided by deployed wireless sensors, data-gathering mobile robots, drones, and satellites to explore complex ecological relationships in local and global dimensions. Its significance lies, for example, in the prediction of plant protection. The paper introduces small smart data logger robots, including the Unmanned Ground Vehicles (robots) developed by the research team. These can replace sensors deployed in the Wireless Sensor Net (WSN).
Open Access: Yes
Applying IoT Sensors and Big Data to Improve Precision Crop Production: A Review
Publication Name: Agronomy
Publication Date: 2023-10-01
Volume: 13
Issue: 10
Page Range: Unknown
Description:
The potential benefits of applying information and communication technology (ICT) in precision agriculture to enhance sustainable agricultural growth were discussed in this review article. The current technologies, such as the Internet of Things (IoT) and artificial intelligence (AI), as well as their applications, must be integrated into the agricultural sector to ensure long-term agricultural productivity. These technologies have the potential to improve global food security by reducing crop output gaps, decreasing food waste, and minimizing resource use inefficiencies. The importance of collecting and analyzing big data from multiple sources, particularly in situ and on-the-go sensors, is also highlighted as an important component of achieving predictive decision making capabilities in precision agriculture and forecasting yields using advanced yield prediction models developed through machine learning. Finally, we cover the replacement of wired-based, complicated systems in infield monitoring with wireless sensor networks (WSN), particularly in the agricultural sector, and emphasize the necessity of knowing the radio frequency (RF) contributing aspects that influence signal intensity, interference, system model, bandwidth, and transmission range when creating a successful Agricultural Internet of Thing Ag-IoT system. The relevance of communication protocols and interfaces for presenting agricultural data acquired from sensors in various formats is also emphasized in the paper, as is the function of 4G, 3G, and 5G technologies in IoT-based smart farming. Overall, these research sheds light on the significance of wireless sensor networks and big data in the future of precision crop production
Open Access: Yes
Challenges of sustainable agricultural development with special regard to Internet of Things: Survey
Publication Name: Progress in Agricultural Engineering Sciences
Publication Date: 2022-12-02
Volume: 18
Issue: 1
Page Range: 95-114
Description:
If we want to increase the efficiency of precision technologies to create sustainable agriculture, we need to put developments and their application on a new footing; moreover, a general paradigm shift is needed. There is a need to rethink close-At-hand and far-off innovation concepts to further develop precision agriculture, from both an agricultural, landscape, and natural ecosystem sustainability perspective. With this, unnecessary or misdirected developments and innovation chains can be largely avoided. The efficiency of the agrotechnology and the accuracy of yield prediction can be ensured by continuously re-planning during the growing season according to changing conditions (e.g., meteorological) and growing dataset. The aim of the paper is to develop a comprehensive, thought-provoking picture of the potential application of new technologies that can be used in agriculture, primarily in precision technology-based arable field crop production, which emphasizes the importance of continuous analysis and optimisation between the production unit and its environment. It should also be noted that the new system contributes to reconciling agricultural productivity and environmental integrity. The study also presents research results that in many respects bring fundamental changes in technical and technological development in field production. The authors believe that treating the subsystems of agriculture, landscape, and natural ecosystem (ALNE) as an integrated unit will create a new academic interdisciplinarity. ICT, emphasizing WSN (Wireless Sensor Network), remote sensing, cloud computing, AI (Artificial Intelligence), economics, sociology, ethics, and the cooperation with young students in education can play a significant role in research. This study treats these disciplines according to sustainability criteria. The goal is to help management fulfil the most important expectation of reducing the vulnerability of the natural ecosystem. The authors believe that this article may be one of the starting points for a new interdisciplinarity, ALNE.
Open Access: Yes
Crop Yield Prediction in Precision Agriculture
Publication Name: Agronomy
Publication Date: 2022-10-01
Volume: 12
Issue: 10
Page Range: Unknown
Description:
Predicting crop yields is one of the most challenging tasks in agriculture. It plays an essential role in decision making at global, regional, and field levels. Soil, meteorological, environmental, and crop parameters are used to predict crop yield. A wide variety of decision support models are used to extract significant crop features for prediction. In precision agriculture, monitoring (sensing technologies), management information systems, variable rate technologies, and responses to inter- and intravariability in cropping systems are all important. The benefits of precision agriculture involve increasing crop yield and crop quality, while reducing the environmental impact. Simulations of crop yield help to understand the cumulative effects of water and nutrient deficiencies, pests, diseases, and other field conditions during the growing season. Farm and in situ observations (Internet of Things databases from sensors) together with existing databases provide the opportunity to both predict yields using “simpler” statistical methods or decision support systems that are already used as an extension, and also enable the potential use of artificial intelligence. In contrast, big data databases created using precision management tools and data collection capabilities are able to handle many parameters indefinitely in time and space, i.e., they can be used for the analysis of meteorology, technology, and soils, including characterizing different plant species.
Open Access: Yes
Predicting Modified Fournier Index by Using Artificial Neural Network in Central Europe
Publication Name: International Journal of Environmental Research and Public Health
Publication Date: 2022-09-01
Volume: 19
Issue: 17
Page Range: Unknown
Description:
The Modified Fournier Index (MFI) is one of the indices that can assess the erosivity of rainfall. However, the implementation of the artificial neural network (ANN) for the prediction of the MFI is still rare. In this research, climate data (monthly and yearly precipitation (pi , Ptotal ) (mm), daily maximum precipitation (Pd-max ) (mm), monthly mean temperature (Tavg ) (°C), daily maximum mean temperature (Td-max ) (°C), and daily minimum mean temperature (Td-min ) (°C)) were collected from three stations in Hungary (Budapest, Debrecen, and Pécs) between 1901 and 2020. The MFI was calculated, and then, the performance of two ANNs (multilayer perceptron (MLP) and radial basis function (RBF)) in predicting the MFI was evaluated under four scenarios. The average MFI values were between 66.30 ± 15.40 (low erosivity) in Debrecen and 75.39 ± 15.39 (low erosivity) in Pecs. The prediction of the MFI by using MLP was good (NSEBudapest(SC3) = 0.71, NSEPécs(SC2) = 0.69). Additionally, the performance of RBF was accurate (NSEDebrecen(SC4) = 0.68, NSEPécs(SC3) = 0.73). However, the correlation coefficient between the observed MFI and the predicted one ranged between 0.83 (Budapest (SC2-MLP)) and 0.86 (Pécs (SC3-RBF)). Interestingly, the statistical analyses promoted SC2 (Pd-max + pi + Ptotal ) and SC4 (Ptotal + Tavg + Td-max + Td-min ) as the best scenarios for predicting MFI by using the ANN–MLP and ANN–RBF, respectively. However, the sensitivity analysis highlighted that Ptotal , pi , and Td-min had the highest relative importance in the prediction process. The output of this research promoted the ANN (MLP and RBF) as an effective tool for predicting rainfall erosivity in Central Europe.
Open Access: Yes
Using the CERES-Maize Model to Simulate Crop Yield in a Long-Term Field Experiment in Hungary
Publication Name: Agronomy
Publication Date: 2022-04-01
Volume: 12
Issue: 4
Page Range: Unknown
Description:
Precision crop production requires accurate yield prediction and nitrogen management. Crop simulation models may assist in exploring alternative management systems for optimizing water, nutrient and microelements use efficiencies, increasing maize yields. Our objectives were: (i) to access the ability of the CERES-Maize model for predicting yields in long-term experiments in Hungary; (ii) to use the model to assess the effects of different nutrient management (different nitrogen rates—0, 30, 60, 90, 120, and 150 kg ha−1). A long-term experiment conducted in Látókép (Hungary) with various N-fertilizer applications allowed us to predict maize yields under different conditions. The aim of the research is to explore and quantify the effects of ecological, biological, and agronomic factors affecting plant production, as well as to conduct basic science studies on stress factors on plant populations, which are made possible by the 30-year database of long-term experiments and the high level of instrumentation. The model was calibrated with data from a long-term experiment field trial. The purpose of this evaluation was to investigate how the CERES-Maize model simulated the effects of different N treatments in long-term field experiments. Sushi hybrid’s yields increased with elevated N concentrations. The observed yield ranged from 5016 to 14,920 kg ha−1 during the 2016–2020 growing season. The range of simulated data of maize yield was between 6671 and 13,136 kg ha−1. The highest yield was obtained at the 150 kg ha−1 dose in each year studied. In several cases, the DSSAT-CERES Maize model accurately predicted yields, but it was sensitive to seasonal effects and estimated yields inaccurately. Based on the obtained results, the variance analysis significantly affected the year (2016–2020) and nitrogen doses. N fertilizer made a significant difference on yield, but the combination of both predicted and actual yield data did not show any significance.
Open Access: Yes
Spatial Variability of Soil Properties and Its Effect on Maize Yields within Field—A Case Study in Hungary
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
Conference report from 13th European Conference on Precision Agriculture (ECPA)
Publication Name: Environmental Sciences Europe
Publication Date: 2021-12-01
Volume: 33
Issue: 1
Page Range: Unknown
Description:
This is a report on the 13th European Conference on Precision Agriculture (ECPA) that took place between 18 and 22, July at the location of the University of Public Service in Budapest, Hungary. The theme of the Conference was the “Adoption of innovative precision agriculture technologies and solutions”. Due to the pandemic, the conference was a hybrid event. The two societies—the International Society of Precision Agriculture and the Hungarian Society of Precision Agriculture—had contributed to the event. The international conference was mainly attended by academic researchers, university instructors, company executives and farmers. The event comprised five plenary and 22 scientific sessions.
Open Access: Yes
Application of spatio-temporal data in site-specific maize yield prediction with machine learning methods
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, P2 O5 , K2 O, 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
Maize yield prediction based on artificial intelligence using spatio-temporal data
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, P2 O5 , K2 O, 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
Soil moisture distribution mapping in topsoil and its effect on maize yield
Publication Name: Biologia Poland
Publication Date: 2017-08-28
Volume: 72
Issue: 8
Page Range: 847-853
Description:
Soil moisture content directly influences yield. Mapping within field soil moisture content differences provides information for agricultural management practices. In this study we aimed to find a cost-effective method for mapping within field soil moisture content differences. Spatial coverage of the field sampling or TDR method is still not dense enough for site-specific soil management. Soil moisture content can be calculated by measuring the apparent soil electrical conductivity (ECa ) using the Veris Soil EC-3100 on-the-go soil mapping tool. ECa is temperature dependent; therefore values collected in different circumstances were standardized to 25°C temperature (EC25 ). Constants for Archie's adjusted law were calculated separately, using soil temperature data. According to our results, volumetric moisture content can be mapped by applying ECa measurements in our particular field with high spatial accuracy. Even though within-field differences occure in the raw ECa map standardization to EC25 is recommended. Soil moisture map was also compared to yield map showing correlation (R2 = 0.5947) between the two datasets.
Open Access: Yes
Effects of soil compaction on cereal yield: A review
Publication Name: Cereal Research Communications
Publication Date: 2017-03-01
Volume: 45
Issue: 1
Page Range: 1-22
Description:
This paper reviews the works related to the effect of soil compaction on cereal yield and focuses on research of field experiments. The reasons for compaction formation are usually a combination of several types of interactions. Therefore one of the most researched topics all over the world is the changes in the soil's physical and chemical properties to achieve sustainable cereal production conditions. Whether we are talking about soil bulk density, physical soil properties, water conductivity or electrical conductivity, or based on the results of measurements of on-line or point of soil sampling resistance testing, the fact is more and more information is at our disposal to find answers to the challenges. Thanks to precision plant production technologies (PA) these challenges can be overcome in a much more efficient way than earlier as instruments are available (geospatial technologies such as GIS, remote sensing, GPS with integrated sensors and steering systems; plant physiological models, such Decision Support System for Agrotechnology Transfer (DSSAT), which includes models for cereals etc.). The tests were carried out first of all on alteration clay and sand content in loam, sandy loam and silt loam soils. In the study we examined especially the change in natural soil compaction conditions and its effect on cereal yields. Both the literature and our own investigations have shown that the soil moisture content changes have the opposite effect in natural compaction in clay and sand content related to cereal yield. These skills would contribute to the spreading of environmental, sustainable fertilizing devoid of nitrate leaching planning and cereal yield prediction within the framework of the PA to eliminate seasonal effects.
Open Access: Yes
Spatiotemporal prediction of soil moisture content at various depths in three soil types using machine learning algorithms
Publication Name: Frontiers in Soil Science
Publication Date: 2025-01-01
Volume: 5
Issue: Unknown
Page Range: Unknown
Description:
Introduction: Accurate prediction of soil moisture content (SMC) is crucial for agricultural systems as it affects hydrological cycles, crop growth, and resource management. Considering the challenges with prediction accuracy and determining the effect of soil texture, depth, and meteorological data on SMC variation and prediction capability of the used models, this research has been conducted. Methods: Three machine learning (ML) models—random forest regression (RFR), eXtreme gradient boosting (XGBoost), and long short-term memory (LSTM)—were developed to predict SMC in three soil types (loam, sandy loam, and silt loam) at five depths of 5, 20, 40, 60, and 80 cm. The dataset was collected during the maize season in 2023, encompassing meteorological parameters collected using Internet of Things (IoT)-based sensors and SMC data calculated using the gravimetric method. Results: The results showed variations in SMC in all studied soil types and depths, with silt loam exhibiting the highest variation in SMC. RFR demonstrated high accuracy at different depths and soil types, particularly in loam soil, at a depth of 80 with a root mean square error (RMSE) value of 0.89 and a mean absolute error (MAE) value of 0.74, and in silt loam at 40 cm depth with an RMSE value of 0.498 and an MAE of 0.416. LSTM performed effectively at shallower and moderate depths (60 and 20 cm) with RMSE values of 0.391 and 0.804 and MAE values of 0.335 and 0.793, respectively. In sandy loam soil at 5 cm depth, XGBoost displayed minimal errors and robust performance at the same depths with higher accuracy, achieving an RMSE of 0.025 and an MAE of 0.159. Analysis of training and validation loss revealed that the LSTM model stabilized and improved with more epochs, showing a more consistent decrease in MSE, while RFR and XGBoost exhibited higher performance with increased model complexity, shown in low MSE and RMSE values. Comparisons between measured and predicted SMC% values demonstrated the models’ effectiveness in capturing soil moisture dynamics. Furthermore, feature importance analysis revealed that solar radiation and precipitation were the most influential predictors across all models, offering critical insights into dominant environmental drivers of soil moisture variability. Discussion: By providing precise SMC predictions across different spatial and temporal scales, this study underscores the value of ML models for SMC prediction, which could have implications for improving irrigation scheduling, reducing water wastages, and enhancing sustainability.
Open Access: Yes
