Bálint Ambrus
57894140200
Publications - 7
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
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
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
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
Calibration of an Arduino-based low-cost capacitive soil moisture sensor for smart agriculture
Publication Name: Journal of Hydrology and Hydromechanics
Publication Date: 2022-09-01
Volume: 70
Issue: 3
Page Range: 330-340
Description:
Agriculture faces several challenges to use the available resources in a more environmentally sustainable manner. One of the most significant is to develop sustainable water management. The modern Internet of Things (IoT) techniques with real-time data collection and visualisation can play an important role in monitoring the readily available moisture in the soil. An automated Arduino-based low-cost capacitive soil moisture sensor has been calibrated and developed for data acquisition. A sensor- and soil-specific calibration was performed for the soil moisture sensors (SKU:SEN0193 - DFROBOT, Shanghai, China). A Repeatability and Reproducibility study was conducted by range of mean methods on clay loam, sandy loam and silt loam soil textures. The calibration process was based on the data provided by the capacitive sensors and the continuously and parallelly measured soil moisture content by the thermogravimetric method. It can be stated that the response of the sensors to changes in soil moisture differs from each other, which was also greatly influenced by different soil textures. Therefore, the calibration according to soil texture was required to ensure adequate measurement accuracy. After the calibration, it was found that a polynomial calibration function (R2 ≥ 0.89) was the most appropriate way for modelling the behaviour of the sensors at different soil textures.
Open Access: Yes
