Bálint Ambrus, Gergely Teschner, Attila Kovács, Miklós Neményi, Lajos Helyes, Zoltán Pék, Sándor Takács, Tarek Alahmad, Anikó Nyéki
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
HELIYON 10: 20 Paper: e37997 , 21 p. (2024)
https://doi.org/10.1016/j.heliyon.2024.e37997
The aim of this study was to estimate field-grown tomato yield 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 and 3D scanning and shape. Field pictures were used for tomato segmentation to determine the ripeness of the crop. A convolution neural network (CNN) model used TensorFlow library was devised for the segmentation of tomato berry 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 Ciclops 3D scanner. The best fitting sphere model was generated using the 3D model. The best 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.
Tarek Alahmad, Miklós Neményi, Anikó Nyéki
“Soil Moisture Content Prediction in Loam Soil with RFR Model”
Acta Agronomica Óváriensis, Vol.65.2. (2024)
https://doi.org/10.17108/ActAgrOvar.2024.65.2.43
Soil moisture content (SMC) is an important factor in agricultural productivity; it has an impact on crop growth, water use efficiency, and soil health. However, accurately predicting SMC, especially at deeper soil layers, remains challenging due to high variability and limited spatiotemporal data resolution. This study developed and evaluated a Random Forest Regression (RFR) model to predict SMC in loam soil at five different depths (5, 20, 40, 60, and 80 cm) utilizing meteorological data (temperature, humidity, precipitation, wind speed, and solar radiation) and vegetation indices: the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI). Data were collected during the maize vegetation season in 2023 in Mosonmagyaróvár, Hungary. The results showed that the mean SMC ranged from 12.61% to 16.19%. Correlation analysis demonstrated that precipitation and NDMI had the strongest positive correlation with SMC, especially at shallower depths r = 0.78 at 5 cm depth, Solar radiation had a moderate correlation with SMC, especially at the deeper depths. The RFR model performed well at all depths, achieving an R² of 0.86 at 5 cm depth; the model accuracy enhanced at deeper layers, achieving R² values of 0.91 and 0.94 at 60 and 80 cm depths, respectively. The most significant predictors according to the feature importance analysis were precipitation, humidity, and NDMI, with NDMI playing a crucial role in subsurface moisture retention at deeper depths. These findings highlight the potential for machine learning algorithms to optimize irrigation approaches and improve water management in precision agriculture.
László Moldvai, Gergely Teschner, Bálint Ambrus, Anikó Nyéki
Technological steps of tomato yield prediction using machine vision
Acta Agronomica Óváriensis (2024)
https://doi.org/10.17108/ActAgrOvar.2024.65.1.89
In this study, we delve into advanced technologies and sensors utilized for precision agriculture, especially in greenhouse environments. Our investigation encompasses information technology, statistical models, and neural network-based approaches for predicting and estimating crop yields, primarily through direct data analysis.
We highlight a significant limitation of current methods: they often do not cover the entire lifecycle of plants, which is critical due to the varying stages of plant development. Although these methods are widely used, their effectiveness can be constrained during the dynamic growth phases of plants, and they are adopted in the absence of better alternatives.
At this point, we introduce machine vision as a versatile tool with applications across numerous fields. Its key advantage lies in its ability to detect changes throughout the plant lifecycle, allowing us to segment the lifecycle into more manageable phases. This segmentation enables the targeted application of statistical, regression, and neural network techniques, with each system focusing on a specific developmental stage.
Machine vision is adept at extracting crucial information at different stages of a plant's life. It can be used for various purposes, such as weed monitoring, tracking plant growth, assessing leaf and stem health, detecting stress and early signs of disease, and evaluating flowering, crop progression, as well as determining maturity, quality, and yield.
To demonstrate the effectiveness of machine vision, we developed a Python application that identifies ripe tomatoes ready for harvest in RGB images. This tool aids in accurately estimating harvest volumes by counting the number of mature tomatoes. Furthermore, we suggest the implementation of a multi-camera system employing machine vision to identify precise agricultural interventions needed at various stages of crop development.
László Moldvai, Gergely Teschner, Bálint Ambrus, Anikó Nyéki
Weed detection in agricultural fields using machine vision
10th International Conference on Agricultural and Biological Sciences (ABS 2024)
https://doi.org/10.1051/bioconf/202412501004
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.
László Moldvai, Péter Ákos Mesterházi, Gergely Teschner, Anikó Nyéki
Weed Detection and Classification with Computer Vision Using a Limited Image Dataset
Applied Sciences 2024, 14 ,4839.
https://doi.org/10.3390/app14114839
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.
Tóth Tamás, Horváth Éva Rita, Dóka Ottó, Kovács Mihály, Fébel Hedvig
The Effects of Mineral Supplementation in Rapeseed Cake Diet on Thyroid Function and Meat Quality in Broiler Chickens
Agriculture 2024, 14, 2333.
https://doi.org/10.3390/agriculture14122333
Rapeseed is a high-quality protein source; however, its quality primarily depends on the variety, origin, and processing method. This study aimed to examine the effectiveness of a mineral supplement (“Peelko”; 27% Ca, 3.5% Mg, 800 mg/kg Fe) in terms of whether it is suitable for reducing the remaining antinutritional substances in cold-pressed rapeseed cake, thereby improving the nutrient content and digestibility of rapeseed. The experiment was carried out with 600 Ross-308 broilers divided into three feeding groups: the control diet contained extracted soybean meal, the R treatment included 10–15% cold-pressed rapeseed cake (in grower and finisher phases), and the R+ treatment consisted of the mineral supplement in addition to the cold-pressed rapeseed cake. R+ had a beneficial effect on the FCR in the grower and finisher feeding phases; moreover, it increased the weight of thyroid glands and the T3 and T4 hormone levels in the blood serum to a lesser extent than R when compared to C (p < 0.05). Diet-specific changes could be observed through the histological examination of thyroid glands, where the acini became larger when the unsupplemented cold-pressed rapeseed cake was fed (R group). Using the mineral supplement (R+ diet) decreased the acinus diameter compared to the R diet, with a similar value to that observed in control birds. The protein content in the breast and fat content in the thigh showed milder changes in R+ than R, compared to C (p < 0.05). The relative ratio of n-6 and n-3 fatty acids narrowed in both R and R+ meat samples compared to C (p < 0.05). R+ may have a more favorable effect on oxidation processes according to the better MDA values in fresh meat (p < 0.001) and samples after 1–2 months of storage (p < 0.05) than R when compared with C. The negative modifications in the color parameters (L*, a*, and b*) and the organoleptic properties of the meat were less significant with R+ than R, compared to the control (p < 0.05). According to the results of this study, the R+ treatment was able to reduce the antinutritional effects of rapeseed, as evident from the properties of the resulting animal products.
Keywords: rapeseed cake; mineral supplement; broilers; thyroid function; meat quality
Induced and field mechanical effects on the hatchability of broiler breeder hatching eggs
Induzierte und feldmechanische Effekte auf den Bruterfolg von Bruteiern von Broiler-Elterntieren
Europ.Poult.Sci., 88. 2024, ISSN 1612-9199, © Verlag Eugen Ulmer, Stuttgart. DOI: 10.1399/eps.2024.397
Egg transport and rough egg handling can have numerous negative effects on hatchability. The authors monitored mechanical effects under field conditions by acceleration sensors and then simulated the same scale effects using a modelling machine to verify the effects on hatchability. To measure and record mechanical effects-instantaneous acceleration (m/s 2), three-dimensional HOBO® Pendant® G Data Logger were used.
The RSS, RSM values calculated from the data from the accelerometer can serve as a point of reference for practitioners to see the mechanical effect level which results in significant negative impact on the hatchability results. It was also revealed that the measurement of the recorded values in the different directions and the minimum and maximum values are important too. Using the HOBO® Pendant® G Data Logger and detailed logging (the exact location of the logger at the time of the technological steps at a given time) can reveal the location of the maximum impact. By analysing RMS x, y, z, the type of impact can be determined. By combining these two pieces of two information, the technological failure can be clearly revealed and corrected. The measurement process described by the authors provides practical advice for hatching egg producers. Moreover, attention is drawn to the short-term damage effect on hatchability, since the 5-minute treatment at 20 Hz, prior incubation significantly reduced the hatchability (P < 0.05), which was achieved at the level of 10.02 RSS m/s 2 and 12.3 m/s 2 maximum value in the direction of x-axis.
It is important for hatching egg producers to be aware that the damage to the mechanical effect is not only visible (broken, cracked eggshell), but can also negatively affect hatchability and thus the profitability of the sector. Furthermore, the typical “spider web” crack on the eggshell clearly refers to the mechanical impact caused by vibration.
Antonella Dalle Zotte , Yazavinder Singh, Eszter Zsedely, Barbara Contiero, Bianca Palumbo, Marco Cullere
Dietary inclusion of defatted silkworm (Bombyx mori L.) pupa meal in broiler chickens: phase feeding effects on nutritional and sensory meat quality
Poultry Science 103, 7, 103812
https://www.sciencedirect.com/science/article/pii/S0032579124003912?via%3Dihub
The present experiment was conducted to test the effect of a 4% defatted silkworm (Bombyx mori) pupae meal (SWM) incorporation into chickens’ diets at different growth phases on meat quality characteristics and sensory traits. Ninety ROSS 308 day-old male broiler chickens were randomly assigned to 3 dietary groups, with 5 replicated pens/diet: the first group received a control (C) diet throughout the growing period of 42 d, the second group received a diet with 4% SWM (SWM1) during the starter phase (1–10 d) and the C diet up to slaughter, whereas the third group was fed the C diet during the starter phase and 4% SWM during the grower and finisher phases (SWM2). Diets were isonitrogenous and isoenergy, and birds had free access to feed and water throughout the experimental trial. At 42 d of age, 15 chickens/treatment were slaughtered at a commercial abattoir. Fatty acid (FA) and amino acid (AA) profiles and contents of meat, as well as its oxidative status, were determined in both breast and leg meat cuts. Also, a descriptive sensory analysis was performed on breast meat by trained panelists. Results highlighted that the SWM2 treatment increased the n-3 proportion and content in both breast and leg meat, thereby improving the omega-6/omega-3 (n-6/n-3) ratio in both cuts (P < 0.001). However, the dietary treatment had no significant effect on the oxidative status of either breast or leg meat (P > 0.05). The SWM had a limited impact on overall sensory traits of breast meat, but it contributed to improve meat tenderness in SWM-fed chickens (P < 0.01). Furthermore, SWM1 meat exhibited higher juiciness (P < 0.05) and off flavor intensity (P < 0.05) compared to the control meat. Overall, the present experiment indicated that defatted SWM holds promise as an alternative ingredient in chicken rations, ensuring satisfactory meat quality. Furthermore, administering SWM during the grower-finisher phase demonstrated beneficial effects on meat healthiness, ultimately enhancing n-3 fatty acids content and reducing the n-6/n-3 ratio.
Tarek Alahmad, Miklós Neményi, Anikó Nyéki
“Applying IoT Sensors and Big Data to Improve Precision Crop Production: A Review”
Agronomy 2023, 13, 2603.
https://doi.org/10.3390/agronomy13102603
Széchenyi István University, Albert Kázmér Faculty of Agricultural and Food Sciences of Széchenyi István University, Department of Biosystems and Precision Technologies, Mosonmagyaróvár 9200, Hungary.
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.
Bálint Ambrus, Miklós Neményi, Attila Kovács, Anikó Nyéki
Development of a Small-Smart Robot in Mosonmagyaróvár
MEZŐGAZDASÁGI TECHNIKA
In recent times, several studies have highlighted the need for another paradigm shift, as the current negative impacts of agriculture on the biosphere cannot be mitigated using knowledge from traditional experiment-based research. Global food security is threatened by several factors, such as population growth, meat consumption trends, and the effects of climate change. Additionally, increasing pest, disease, and weed tolerance are placing greater pressure on both traditional and precision technologies. To alleviate the burden of these challenges, there is an opportunity to automate and robotize certain aspects of the farming process.
Zoltán Molnár, Mutum Lamnganbi, Wogene Solomon, Tibor Janda
Chitosan and cyanobacterial biomass accounting physiological and biochemical development of winter wheat (Triticum aestivum L.) under nutrient stress conditions
Agrosystems, Geosciences & Environment, 6, e20428 (2023)
https://doi.org/10.1002/agg2.20428
In the spirit of returning to nature and using science to increase crop productivity without posing any threat to the environment, researchers are paying attention to making natural products alternative sources of nutrients for plants at affordable prices. On top of this, chitosan and cyanobacteria have become popular in agriculture as metabolic enhancers, biofertilizers, and antimicrobial properties. Cyanobacteria are known to possess bio stimulating properties, whereas chitosan is well known for its inherent biological properties. To minimize nitrogen application, this experiment was conducted for the first time to check whether application of chitosan, microalgae or both with 50% nitrogen can balance the nutrient requirement for different physiological and biochemical development as effectively as a 100% nitrogen dose. The data were recorded only for the early vegetative stages, as the seeds were non-vernalized. The basic parameters recorded were hexose content, chlorophyll a, chlorophyll b, total phenol content (TPC) and relative water content (RWC). In most parameters, comparable results were found between the control (with a 100% recommended nitrogen dose) and other treatments (where either microalga, chitosan, or both were added). Whereas it was clearly shown that 50% of recommended nitrogen doses reduce the hexose, chlorophyll, and relative water contents. Thus, the treatments were effective in supplementing the developmental requirements. Therefore, the combined use of chitosan and cyanobacteria in crops significantly reduces nitrogen fertilization, increases photosynthesis, increases resistance to water stress, and increases antioxidant activity in modern agriculture.
Levente Vörös, Rita Ledóné Ábrahám
Effect of azadirachtin applied as seed dressing on the larval density of and root injury caused by the western corn rootworm/Diabrotica virgifera virgifera
J Plant Dis Prot 130, 757–767 (2023)
https://doi.org/10.1007/s41348-023-00763-3
The western corn rootworm (Diabrotica virgifera virgifera LeConte) is one of the most important pests of maize in Hungary. As both larvae and imagoes are capable of causing major economic losses, their control in continuous maize cropping systems is essential. The control of larvae is costly and the related use of large doses of soil disinfectants places an increased burden on the environment. In recent years, several chemical products used as soil insecticides and seed dressings have been phased out, thus increasing the value of environmentally friendly biological products that provide effective protection against the pest. The active ingredient azadirachtin, the extract of the seeds of Azadirachta indica is one of such biological agents. In our experiments, we studied the efficacy of two azadirachtin products, Neemazal T/S (1% azadirachtin; 10 g/l) and Neemazal F (5% azadirachtin; 50 g/l) used as seed dressing against western corn rootworm larvae. The products were used in different concentrations (10–150%) in different regions and on various soil-types in Hungary. The active ingredient could effectively control the pest in its larval stage. Treatments with concentrations exceeding 50% were effective in all the replications.
János Tenke, Orsolya Vida, István Nagy, János Tossenberger
Classifying Genetic Lines in Pork Production by Ileal Crude Protein and Amino Acid Digestibility in Growing Pigs Animals
(Basel) 2023 Jun 6;13(12):1898.
https://doi.org/10.3390/ani13121898
https://www.mdpi.com/2076-2615/13/12/1898
The first aim of the study was to evaluate the effect of different dietary lysine (LYS) to energy (DE) ratios on the apparent ileal digestibility (AID) of crude protein (CP) and selected amino acids (AA) in growing pigs (40-60 kg) of different genotypes. The second aim was to classify genotypes into groups based on the AID of CP and AAs. The trials were conducted on a total of 90 cross-bred barrows (30 animals/genotype) in two replicates. Before the trial series, the experimental animals (average initial body weight (BW) = 40.9 ± 8.5 kg) were surgically fitted with post valve T-cannula (PVTC). The diets were formulated with six different total LYS/DE ratios. Titanium dioxide (TiO2) was added to the diets (5 g/kg) as an indigestible marker. Based on our results, it can be concluded that the LYS/DE ratio of the diets affected the AID of the CP and AA in different ways by each genotype (p < 0.05). It can also be concluded that pigs of different genetic potential can be classified with a high accuracy (91.7%) in respect of their CP and AA digestive capacity. Our results indicate the development of genetic-profile-based swine nutrition technologies as a future direction.
Judit Márton, Ferenc Szabó
Some Actualities and Challenges in Sustainable Beef Cattle Breeding and Husbandry
Chemical Engineering Ttransactions, 107, 2023, 241-246., DOI:10.3303/CET2310704
Beef cattle farming is an environmentally friendly food-producing animal husbandry sector that is based largely on pasture and arable by-product feedstuffs. It faces several problems that need to be addressed from a sustainability point of view. Population growth and growing food demand raise concerns about the environmental consequences of expanding beef production using current systems. Consequently, this condition underscores the importance of maintaining an equilibrium between these sustainability pillars and the necessity of adopting more sustainable models. This review article analyzes the most important, recent literary sources dealing with the sustainability of the sector. As a result of the literary synthesis, it has been established that most experts emphasize the two main pillars of sustainability, namely, economic and environmental aspects. The present work directs attention to the third and possible fourth point, the social as well as the cultural aspects of the sustainability of the beef cattle sector, which will be increasingly important to keep in mind in the future.
Tamás Csürhés, Ferenc Szabó, Gabriella Holló, Edit Mikó, Márton Török,Szabolcs Bene
Relationship between Some Myostatin Variants and Meat Production Related Calving, Weaning and Muscularity Traits in Charolais Cattle
Animals 2023, 13, 1895. https://doi.org/10.3390/ani13121895
The slaughter value on live cattle can be assessed during visual conformation scoring, as well as by examining different molecular genetic information, e.g. myostatin gene, which can be responsible for muscle development. In this study F94L, Q204X, nt267, nt324 and nt414 alleles of the myostatin gene (MSTN) were examined in relation to birth weight (BIW), calving ease (CAE), 205-day weaning weight (CWW), muscle score of shoulder (MSS), muscle score of back (MSB), muscle score of thigh (MST), roundness score of thigh (RST), loin thickness score (LTS), and overall muscle development percentage (OMP) of Charolais weaned calves in Hungary. Multi-trait analysis of variance (GLM) and weighted linear regression analysis were used to process the data. Calves carrying the Q204X allele in heterozygous form achieved approximately 0.14 points higher MSB, MST and LTS and 1.2% higher OMP and gained 8.56 kg more CWW than their counterparts not carrying the allele (p<0.05). As for the F94L allele, there was a difference of 4.08 kg in CWW of the heterozygous animals, but this difference could not be proved statistically. The other alleles had no significant effect on the evaluated traits.
Nyéki Anikó, Kerepesi Csaba, Daróczy Bálint, Benczúr András, Milics Gábor, Nagy J., Harsányi Endre, Kovács Attila József, Neményi Miklós
Application of spatio‑temporal data in site‑specific maize yield prediction with machine learning methods
Precision Agriculture (2021) 22:1397–1415
https://doi.org/10.1007/s11119-021-09833-8
Abstract
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 artifcial 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 fnd the best solution, various models were used: counter-propagation artifcial neural networks (CP-ANNs), XYfused Querynetworks (XY-Fs), supervised Kohonen networks (SKNs), neural networks with Rectangular Linear Activations (ReLU), extreme gradient boosting (XGBoost), support-vector machine (SVM), and diferent subsets of the independent variables in fve 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 feld. 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 diferent 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 infuence of the spatial distribution of site-specifc soil fertility on maize grain yields. This paper provides a new method of spatio-temporal data analyses, taking the most important infuencing factors on maize yields into account.
Keywords Maize yield prediction · Machine learning methods · Gradient boosting (XGBoost) · Random felds · Yield infuencing variables · Decision support in crop production
AMBRUS BÁLINT
APPLICATION POSSIBILITIES OF ROBOT TECHNIQUE IN ARABLE PLANT PROTECTION.
Széchenyi István University, Albert Kázmér Faculty of Agricultural and Food Sciences of Széchenyi István University, Department of Biosystems and Precision Technologies, Mosonmagyaróvár
Acta Agronomica Óváriensis Vol. 62. No.1. (2021)
SUMMARY
The author presents the characteristics of robots used in agriculture, including plant protection, as well as the possibilities of robot technology and the possibilities provided by robot technology. It places particular emphasis on the analysis of sustainability criteria. The most critical factor is the reduction of the use of synthetic pesticides and chemicals replacement by physical procedures. This knowledge helps the paradigm shift in the automatization and robotization of agricultural work. The future belongs to small, smart, interconnected, light machines. However, their properties raise moral and, within that, a number of ethical, management and social issues, both in the operation and in the design and manufacture of the machines working in the swarm. The future also belongs to the super-intelligent machines in this field, which will be able to improve themselves including software and hardware too. The dissertation also draws attention to the fact that a paradigm shift is needed in the sense that basic machines must be standardized, ie the design of a robot should not start from the basics, but can also use previously developed applications. A good example of this is From Toy to Tool: FTtT. Industrial robots offer many opportunities for innovation, but at the same time adapting this knowledge and experience to the natural environment poses serious challenges. Due to the specific nature robotics in the industry can only be partially utilized here, and in most of the individual production areas special solutions are also needed. It is likely that the robotization of agriculture will also require the development of new organizational and organizational structures. The article also helps with this.
Keywords: robot, robotics, plant protection, GPS, RTK, MI, drone