Wireless sensor networks (WSNs), IoT-enabled sensing, and mobile platforms are increasingly used in precision agriculture, but fixed stations cannot fully capture within-field or canopy-level variability. This study developed and greenhouse-tested a low-cost modular tracked robot as a wireless environmental-sensing and telemetry research node for future crop-monitoring applications, rather than as a fully validated autonomous field robot. An open-source tracked chassis was extended with Raspberry Pi edge computing, a Cube Orange autopilot, RTK-capable GNSS, 5G/VPN/MAVLink communication, and BME280, BH1750, MLX90614, RGB camera, and LiDAR-ready sensing. The platform measured 35 & times; 25 & times; 40 cm, weighed 6.4 kg, operated from a 12 V supply, and provided about 4 h of runtime under favorable conditions. Sensor data were logged locally and could be transmitted remotely, while telemetry was visualized in QGroundControl. The environmental sensing layer was compared with a calibrated Libelium Smart Agriculture Pro station in a greenhouse using 70 synchronized samples per variable across three sessions. Because the two nodes were placed close to one another but were not strictly co-located, the comparison quantifies operational sensing differences under greenhouse microclimatic gradients rather than pure laboratory sensor error. Regression was retained only as a trend-tracking metric, while method-comparison interpretation was added using bias and Bland-Altman limits of agreement. The pressure channel showed strong trend tracking (R2 = 0.992, RMSE = 0.024 hPa), whereas air temperature (R2 = 0.756, RMSE = 2.537 degrees C) and relative humidity (R2 = 0.817, RMSE = 5.024%) were suitable mainly for exploratory microclimate mapping and relative trend monitoring unless local calibration is applied. The title, claims and conclusions were therefore narrowed to greenhouse sensing-layer validation and future crop-monitoring deployment.

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.

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.

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.

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.

This work aims to compare and statistically analyze Normalized Difference Vegetation Index (NDVI) values provided by GreenSeeker handheld crop sensor measurements and calculate NDVI values derived from the MicaSense RedEdge-MX Dual Camera, to predict in-season winter wheat (Triticum aestivum L.) yield, improving a yield prediction model with cumulative growing degree days (CGDD) and days from sowing (DFS) data. The study area was located in Mosonmagyaróvár, Hungary. A small-scale field trial in winter wheat was constructed as a randomized block design including Environmental: N-135.3, P2O5-77.5, K2O-0; Balance: N-135.1, P2O5-91, K2O-0; Genezis: N-135, P2O5-75, K2O-45; and Control: N, P, K 0 kg/ha. The crop growth was monitored every second week between April and June 2022 and 2023, respectively. NDVI measurements recorded by GreenSeeker were taken at three pre-defined GPS points for each plot; NDVI values based on the MicaSense camera Red and NIR bands were calculated for the same points. Results showed a significant difference (p ≤ 0.05) between the Control and treated areas by GreenSeeker measurements and Micasense-based calculated NDVI values throughout the growing season, except for the heading stage. At the heading stage, significant differences could be measured by GreenSeeker. However, remotely sensed images did not show significant differences between the treated and Control parcels. Nevertheless, both sensors were found suitable for yield prediction, and 226 DAS was the most appropriate date for predicting winter wheat’s yield in treated plots based on NDVI values and meteorological data.

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.

A precíziós növénytermesztésben egyre több adatot mérünk, egyre több adatot gyűjtünk. Az adatgyűjtés végső célja, hogy valamilyen precíziós beavatkozást el tudjunk végezni. Tudjunk szabályozni bizonyos környezeti paramétereket adott szempontrendszer szerint optimálisan. A gyakorlatban is felhasználható eredményekhez szükségünk van a várható hozam minőségi és mennyiségi paramétereire, a betakarítás várható időpontjára. A monitoring során képződött adatokból bizonyos betegségek előrejelzését is tudjuk pontosítani, illetve s stresszhatásokat is felismerhetjük. Emellett olyan kérdésekre is igyekszünk választ adni, hogy szabályozható-e bizonyos minőségben a környezet és tudjuk-e optimalizálni a beavatkozási technológiákat? Jelen munkánknak az volt a célja, hogy meghatározzuk, milyen adatokat tudunk előállítani gépi látás segítségével képfeldolgozás útján. Igyekeztünk meghatározni, hogy a precíziós növénytermesztésben milyen adatok állhatnak rendelkezésünkre, mire alkalmazhatjuk a képfeldolgozási megoldásokat és a gépi látást. Vannak olyan szenzorok, amelyek direkt adatokkal szolgáltatnak és vannak olyanok, amelyek adataiból indexeket vagy konkrét transzformációk útján nyerünk feldolgozható információkat. Célkitűzésünk ezen technológiák áttekintése, hogy a gépi látás témakörében elmélyedve konkrét adatokat és információkat nyerjünk ki, amelyek már a precíziós beavatkozást lehetővé teszik.

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.

Abstract“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).

Protecting agricultural fields, like crops, vineyards, and husbandry areas, has been a difficult challenge since historical times. Classical methods to prevent intrusion are often destructive to wild and domestic animals alike. Even more current nondestructive systems, like camera-based systems are attributed to specific problems related to environmental or technological issues. Furthermore, verifying the effectiveness of installed systems is difficult, as the triggering situations are unmanageable and typically occur unsupervised. This paper presents a complex vision-based intrusion detection system to overcome these problems and further proposes more extensive control and flexibility on the development process. The solution provides a workflow integrating Digital Reality methods into the system development by creating a digital twin of the drone and its surrounding environment in a general-purpose robotic simulator. With this simulation, the triggering events and environmental effects can be easily emulated, such as a wild animal entering the area of interest. The solution also focuses on incorporating new 5G info-communication networks on handling communication between the intrusion detection system and the base station in a distributed manner.

AbstractIf 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.

: The aim of this study is to develop a decision support system using sensors and IoT (Internet of Things)-based application in precision agriculture, first of all, in Hungary. These new technologies have the potential to transform decision-making in agriculture and to reduce environmental impact. In this paper, the advantages are demonstrated of two types of sensor sets that were installed to collect data from soil, plant, and the environment. Data are processed in a decision-making system. The proposed system allows users to monitor the fields in real-time and gives recommendations for production using wireless and internet communication.

A hat – kulonboző tehenletszamu es termelesi szinvonalu – tarsas gazdasagban vegzett vizsgalatainkbol megallapithato, hogy meg a nagy – 8 ezer liter feletti – fajlagos tejhozam mellett is csak a kiegeszitő (kvota-) tamogatasok tettek a tejtermelest nyeresegesse. A tejtermeleshez adott kulonfele tamogatasok nelkul a hazai tejtermelők egyre nagyobb hanyada termel vesztesegesen. A vizsgalt hat gazdasagbol kettő a teljes allomanyat felszamolta 2010-ben. A tejtermeles koltsegeiben tovabbra is a takarmanykoltsegek adjak a legnagyobb (52%-os) reszt, ami a magas tejhozamu (8 ezer liter feletti) allomanyokban meg ennel is nagyobb. A magasabb hozamok ugyanakkor merseklőleg hatottak a munkaber-, az amortizacios es az altalanos koltsegek aranyara. A tejtermeles koltsegei het ev alatt (2004–2010) a tarsas gazdasagokban orszagosan kozel 20, a takarmanykoltsegek 14, a munkaberkoltsegek pedig 66%-kal emelkedtek. A tej ertekesitesi ara ebben az időszakban – a 2007–2008. evek kivetelevel – stagnalt, illetve 2009-ben 2004-hez kepest 5, 2008-hoz kepest viszont 25%-kal csokkent. 2010-ben a 2009. evihez kepest 13, 2011 első negyedeveben pedig kozel 50%-os tejarnovekedes kovetkezett be, amihez a tamogatasok 70%-os emelkedese tarsult. --------------------------------------------- Our study of the six dairy farms – with different number of cows and at various standards of production – run by partnerships found that even when milk yields were high (above 8,000 litres), milk production was only made profitable by the additional (quotabased) subsidies. When discounting the various subsidies granted for milk production, an increasing percentage of Hungarian dairy farmers make a loss. Two of the six farms studied liquidated their entire livestock in 2010. Feed costs remain the largest component of dairy production at 52% - and even higher in herds with a high milk yield (above 8.000 litres). At the same time, higher yields reduced the share of wage costs, amortisation and general costs. Over a seven-year period (2004 to 2010) the costs of dairy production increased by nearly 20% nationally at farms run by partnerships, with feed costs going up by 14% and wage costs by 66%. The selling price of milk, however, remained stagnant in the same period (except in 2007-2008); in 2009 it was 5% lower than in 2004 and 25% lower than in 2008. The selling price of milk rose by 13% from 2009 to 2010 and by nearly 50% to the first quarter of 2011, accompanied by a 70% increase in subsidies.

A hat – kA¼lA¶nbA¶zA‘ tehA©nlA©tszAimAo A©s termelA©si szA­nvonalAo – tAirsas gazdasAigban vA©gzett vizsgAilatainkbA³l megAillapA­thatA³, hogy mA©g a nagy – 8 ezer liter feletti – fajlagos tejhozam mellett is csak a kiegA©szA­tA‘ (kvA³ta-) tAimogatAisok tettA©k a tejtermelA©st nyeresA©gessA©. A tejtermelA©shez adott kA¼lA¶nfA©le tAimogatAisok nA©lkA¼l a hazai tejtermelA‘k egyre nagyobb hAinyada termel vesztesA©gesen. A vizsgAilt hat gazdasAigbA³l kettA‘ a teljes AillomAinyAit felszAimolta 2010-ben. A tejtermelA©s kA¶ltsA©geiben tovAibbra is a takarmAinykA¶ltsA©gek adjAik a legnagyobb (52%-os) rA©szt, ami a magas tejhozamAo (8 ezer liter feletti) AillomAinyokban mA©g ennA©l is nagyobb. A magasabb hozamok ugyanakkor mA©rsA©klA‘leg hatottak a munkabA©r-, az amortizAiciA³s A©s az AiltalAinos kA¶ltsA©gek arAinyAira. A tejtermelA©s kA¶ltsA©gei hA©t A©v alatt (2004–2010) a tAirsas gazdasAigokban orszAigosan kA¶zel 20, a takarmAinykA¶ltsA©gek 14, a munkabA©rkA¶ltsA©gek pedig 66%-kal emelkedtek. A tej A©rtA©kesA­tA©si Aira ebben az idA‘szakban – a 2007–2008. A©vek kivA©telA©vel – stagnAilt, illetve 2009-ben 2004-hez kA©pest 5, 2008-hoz kA©pest viszont 25%-kal csA¶kkent. 2010-ben a 2009. A©vihez kA©pest 13, 2011 elsA‘ negyedA©vA©ben pedig kA¶zel 50%-os tejAirnA¶vekedA©s kA¶vetkezett be, amihez a tAimogatAisok 70%-os emelkedA©se tAirsult. --------------------------------------------- Our study of the six dairy farms – with different number of cows and at various standards of production – run by partnerships found that even when milk yields were high (above 8,000 litres), milk production was only made profitable by the additional (quotabased) subsidies. When discounting the various subsidies granted for milk production, an increasing percentage of Hungarian dairy farmers make a loss. Two of the six farms studied liquidated their entire livestock in 2010. Feed costs remain the largest component of dairy production at 52% - and even higher in herds with a high milk yield (above 8.000 litres). At the same time, higher yields reduced the share of wage costs, amortisation and general costs. Over a seven-year period (2004 to 2010) the costs of dairy production increased by nearly 20% nationally at farms run by partnerships, with feed costs going up by 14% and wage costs by 66%. The selling price of milk, however, remained stagnant in the same period (except in 2007-2008); in 2009 it was 5% lower than in 2004 and 25% lower than in 2008. The selling price of milk rose by 13% from 2009 to 2010 and by nearly 50% to the first quarter of 2011, accompanied by a 70% increase in subsidies.