Smart solutions in agricultural robotics

• Autorzy: Kiktev Nikolay , Vasylenko Oksana , Horetska Iryna , Panchenko Anatolii , Slobodian Sergii , Kuboń Maciej , Skibko Zbigniew , Hutsol Taras

Identyfikatory

DOI

10.2478/agriceng-2025-0010

Warianty tytułu

PL

Inteligentne rozwiązania w robotyce rolniczej

• Języki publikacji: EN

Abstrakty

EN

The article contains an overview and prospects for the introduction of smart and robotic technologies in the agricultural industry. Today, robotization of agriculture and food technology is not a leading industry compared to robotization of other sectors of the economy. Analytical review of the global robotics market revealed that robots in the food industry account for 2% of the total, and robots for agricultural operations account for less than 1%. We analyzed global trends in robotization and the introduction of artificial intelligence in agriculture in various countries. A review of smart robotic technologies, in particular, smart sensors and actuators, the Internet of Things, cloud technologies, methods of data analysis, forecasting, classification, and image recognition using neural networks, is provided. Methods and means are considered in relation to both crop production and livestock production. Four main classes of intelligent technologies in agriculture were identified: robotics, Internet of Things, machine learning, and UAVs. In crop production, robots are common in harvesting, controlling weeds, processing trees, monitoring trees at different stages of the growing season, including leaf and fruit diseases, counting the number of flowers and fruits, and other operations. Identification of fruits, leaves, weeds, as well as diseases of fruits and leaves is performed using computer vision, and in recent years convolutional neural networks have become widespread. Field monitoring is performed using UAVs with subsequent image processing using spectral analysis methods. In livestock farming, smart technologies are represented by feeding and milking robots, livestock monitoring, detection of predators, navigation in livestock buildings, monitoring animal health and behavior. Smart technologies are also used in fish farming to create smart farms. In this mini review, we have tried to integrate advanced smart and robotic technologies for various agricultural operations.

PL

Artykuł stanowi przegląd inteligentnych i zautomatyzowanych technologii stosowanych w przemyśle rolniczym. Jego celem jest analiza perspektyw wdrożenia i integracji najnowszych rozwiązań z zakresu inteligentnych i zrobotyzowanych technologii wspierających różne aspekty tego sektora. Obecnie poziom robotyzacji rolnictwa i produkcji żywności pozostaje niski w porównaniu z innymi sektorami gospodarki. Zgodnie z danymi z globalnego rynku robotyki, roboty wykorzystywane w przemyśle spożywczym stanowią jedynie 2% ogółu, a udział robotów w operacjach rolniczych nie przekracza 1%. W artykule przeanalizowano światowe trendy w zakresie robotyzacji i wdrażania sztucznej inteligencji w rolnictwie, z uwzględnieniem doświadczeń różnych krajów. Przedstawiono przegląd technologii inteligentnej robotyki, w tym: inteligentnych czujników i siłowników, Internetu rzeczy (IoT), technologii chmurowych, metod analizy danych, prognozowania, klasyfikacji oraz rozpoznawania obrazów przy użyciu sieci neuronowych. Omówiono ich zastosowanie zarówno w produkcji roślinnej, jak i zwierzęcej. Wyróżniono cztery główne klasy inteligentnych technologii w rolnictwie: robotykę, Internet rzeczy, uczenie maszynowe oraz bezzałogowe statki powietrzne (drony). W produkcji roślinnej roboty są stosowane m.in. w zbiorach, zwalczaniu chwastów, pielęgnacji i monitorowaniu drzew w różnych fazach sezonu wegetacyjnego - w tym w wykrywaniu chorób liści i owoców, liczeniu kwiatów i owoców oraz innych procesach. Identyfikacja owoców, liści, chwastów i chorób odbywa się przy użyciu technik wizji komputerowej, a w ostatnich latach szczególną popularność zyskały konwolucyjne sieci neuronowe. Monitoring upraw realizowany jest za pomocą dronów, a pozyskane obrazy poddawane są analizie spektralnej. W produkcji zwierzęcej inteligentne technologie obejmują roboty do karmienia i dojenia, systemy monitorowania zwierząt gospodarskich, detekcję drapieżników, nawigację w budynkach inwentarskich oraz monitorowanie stanu zdrowia i zachowań zwierząt. Zaawansowane technologie znajdują również zastosowanie w hodowli ryb - w tworzeniu inteligentnych gospodarstw akwakultury.

Słowa kluczowe

EN

robot; artificial intelligence; IT; neural network; digital agriculture; crop production; animal husbandry

PL

robot; sztuczna inteligencja; IT; sieć neuronowa; rolnictwo cyfrowe; produkcja roślinna; hodowla zwierząt

• Wydawca: Polskie Towarzystwo Inżynierii Rolniczej
• Czasopismo: Agricultural Engineering
• Rocznik: 2025
• Tom: Vol. 29, No. 1
• Strony: 157--186
• Opis fizyczny: Bibliogr. 70 poz., rys.

Twórcy

autor

Kiktev Nikolay

• Department of Automation and Robotic Systems, National University of Life and Environmental Science of Ukraine, 03-041 Kyiv, Ukraine

• https://orcid.org/0000-0001-7682-280X

autor

Vasylenko Oksana

• Departments of Electrical Engineering, Mechanical Engineering and Engineering Management, Anhalt University of Applied Sciences, Bernburger Str. 57, 06366 Köthen, Germany

• https://orcid.org/0000-0001-9582-7980

autor

Horetska Iryna

• Department of Agricultural Engineering, Odesa State Agrarian University, 65-012 Odesa, Ukraine
• Ukrainian University in Europe - Foundation, Balicka 116, 30-149 Krakow, Poland

• https://orcid.org/0000-0002-3639-9784

autor

Panchenko Anatolii

• Department of Mechatronic Systems of Tractors and Agricultural Machines, Dmytro Motornyi Tavria State Agrotechnological University, 18, B. Khmelnitsky Аve., 72310 Melitopol, Ukraine

• https://orcid.org/0000-0002-1230-1463

autor

Slobodian Sergii

• Department of Information Technology, Physical, Mathematical and Civil Defence Disciplines, Faculty of Energy and Information Technologies, Higher Educational Institution “Podillia State University”, 32300 Kamianets-Podilskyi, Ukraine

• https://orcid.org/0000-0001-5758-0147

autor

Kuboń Maciej

• Department of Production Engineering, Logistics and Applied Computer Science, Faculty of Production and Power Engineering, University of Agriculture in Krakow, Balicka 116B, 30-149 Krakow, Poland
• Eastern European State College of Higher Education in Przemysl, Ksiazat Lubomirskich 6, 37-700 Przemysl, Poland

• https://orcid.org/0000-0003-4847-8743

autor

Skibko Zbigniew

• Faculty of Electrical Engineering, Bialystok University of Technology, Wiejska 45, 15-351 Bialystok, Poland

• https://orcid.org/0000-0002-5238-6019

autor

Hutsol Taras

• Department of Machine Operation, Ergonomics and Production Processes, Faculty of Production and Power Engineering, University of Agriculture in Krakow, Balicka 116B, 30-149 Krakow, Poland

• https://orcid.org/0000-0002-9086-3672

Bibliografia

• Green Deal. Available online: https://ec.europa.eu/info/strategy/priorities/2019-2024/european-greendeal (accessed on 10 February 2024).
• Afonso, M., Fonteijn, H., Fiorentin, F.S., Lensink, D., Mooij, M., Faber, N., Polder, G., Wehrens, R. (2020). Tomato Fruit Detection and Counting in Greenhouses Using Deep Learning. Frontiers in plant science, 11, 1759. https://doi.org/10.3389/fpls.2020.571299.
• Aggarwal, M., Khullar, V., Goyal, N., Gautam, R., Alblehai, F., Elghatwary, M., Singh, A. (2023). Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets. Agronomy, 13, 2483. https://doi.org/10.3390/agronomy13102483.
• Analytical review of the global robotics market 2019 [Analiticheskij obzor mirovogo rynka robototehniki 2019]. Online: https://www.sberbank.ru/common/img/uploaded/pdf/sberbank_robotics_review_2019_17.07.2019_m.pdf Accessed on 13.03.2023 (accessed on 10 February 2024).
• Andriyanov, N. (2023). Development of Apple Detection System and Reinforcement Learning for Apple Manipulator. Electronics, 12, 727. https://doi.org/10.3390/electronics12030727.
• Andriyanov, N., Khasanshin, I., Utkin, D., Gataullin, T., Ignar, S., Shumaev, V., Soloviev, V. (2022). Intelligent System for Estimation of the Spatial Position of Apples Based on YOLOv3 and Real Sense Depth Camera D415. Symmetry, 14, 148. https://doi.org/10.3390/sym14010148.
• Assunção, E., Gaspar, P.D., Alibabaei, K., Simões, M.P., Proença, H., Soares, V.N.G.J., Caldeira, J.M.L.P. (2022). Real-Time Image Detection for Edge Devices: A Peach Fruit Detection Application. Future Internet, 14, 323. https://doi.org/10.3390/fi14110323.
• Avgoustaki, D.D., Avgoustakis, I., Miralles, C.C., Sohn, J., Xydis, G. (2022). Autonomous Mobile Robot with Attached Multispectral Camera to Monitor the Development of Crops and Detect Nutrient and Water Deficiencies in Vertical Farms. Agronomy, 12, 2691. https://doi.org/10.3390/agronomy12112691.
• Bao, X., Niu, Y., Li, Y., Mao, J., Li, S., Ma, X., Yin, Q., Chen, B. (2022). Design and Kinematic Analysis of Cable-Driven Target Spray Robot for Citrus Orchards. Applied Sciences, 12, 9379. https://doi.org/10.3390/app12189379.
• Bausewein, M., Mansfeld, R., Doherr, M.G., Harms, J., Sorge, U.S. (2022). Sensitivity and Specificity for the Detection of Clinical Mastitis by Automatic Milking Systems in Bavarian Dairy Herds. Animals, 12, 2131. https://doi.org/10.3390/ani12162131.
• Bi, C., Hu, N., Zou, Y., Zhang, S., Xu, S., Yu, H. (2022). Development of Deep Learning Methodology for Maize Seed Variety Recognition Based on Improved Swin Transformer. Agronomy, 12, 1843. https://doi.org/10.3390/agronomy12081843.
• Bist, R.B., Subedi, S., Yang, X., Chai, L. (2023). A Novel YOLOv6 Object Detector for Monitoring Piling Behavior of Cage-Free Laying Hens. AgriEngineering, 5, 905-923. https://doi.org/10.3390/agriengineering5020056.
• Chang, C.-L., Xie, B.-X., Wang, C.-H. (2020). Visual Guidance and Egg Collection Scheme for a Smart Poultry Robot for Free-Range Farms. Sensors, 20, 6624. https://doi.org/10.3390/s20226624.
• Chen, C.-H., Wu, Y.-C., Zhang, J.-X., Chen, Y.-H. (2022). IoT-Based Fish Farm Water Quality Monitoring System. Sensors, 22, 6700. https://doi.org/10.3390/s22176700.
• Dac, H.H., Gonzalez Viejo, C., Lipovetzky, N., Tongson, E., Dunshea, F.R., Fuentes, S. (2022). Livestock Identification Using Deep Learning for Traceability. Sensors, 22, 8256. https://doi.org/10.3390/s22218256.
• Džermeikaitė, K., Bačėninaitė, D., Antanaitis, R. (2023). Innovations in Cattle Farming: Application of Innovative Technologies and Sensors in the Diagnosis of Diseases. Animals, 13, 780. https://doi.org/10.3390/ani13050780.
• Emmi, L., Fernández, R., Gonzalez-de-Santos, P., Francia, M., Golfarelli, M., Vitali, G., Sandmann, H., Hustedt, M., Wollweber, M. (2023). Exploiting the Internet Resources for Autonomous Robots in Agriculture. Agriculture, 13, 1005. https://doi.org/agriculture13051005.
• Fonteijn, H., Afonso, M., Lensink, D., Mooij, M., Faber, N., Vroegop, A., Polder, G., Wehrens, R. (2021). Automatic Phenotyping of Tomatoes in Production Greenhouses Using Robotics and Computer Vision: From Theory to Practice. Agronomy, 11, 1599. https://doi.org/10.3390/agronomy 11081599.
• Fukada, K., Hara, K., Cai, J., Teruya, D., Shimizu, I., Kuriyama, T., Koga, K., Sakamoto, K., Nakamura, Y., Nakajo, H. (2023). An Automatic Tomato Growth Analysis System Using YOLO Transfer Learning. Applied Sciences, 13, 6880. https://doi.org/10.3390/app13126880.
• Gálik, R., Lüttmerding, G., Boďo, Š., Knížková, I., Kunc, P. (2021). Impact of Heat Stress on Selected Parameters of Robotic Milking. Animals, 11, 3114. https://doi.org/10.3390/ani11113114.
• Ghosh, P.K., Sundaravadivel, P. (2023). Stretchable Sensors for Soft Robotic Grippers in Edge-Intelligent IoT Applications. Sensors, 23, 4039. https://doi.org/10.3390/s23084039.
• Hutsol, T., Kutyrev, A., Kiktev, N., Biliuk, M. (2023). Robotic Technologies in Horticulture: Analysis and Implementation Prospects. Agricultural Engineering, 27, 113-133. https://doi.org/ 10.2478/agriceng-2023-0009.
• Ji, W., He, G., Xu, B., Zhang, H., Yu, X. (2024). A New Picking Pattern of a Flexible Three-Fingered End-Effector for Apple Harvesting Robot. Agriculture, 14, 102. https://doi.org/10.3390/agriculture 14010102.
• Jo, J.-H., Nejad, J.G., Lee, J.-S., Lee, H.-G. (2022). Evaluation of Heat Stress Effects in Different Geographical Areas on Milk and Rumen Characteristics in Holstein Dairy Cows Using Robot Milking and Rumen Sensors: A Survey in South Korea. Animals, 12, 2398. https://doi.org/10.3390/ani12182398.
• Kamon, S., di Maria, E., Giannoccaro, N.I., Ishii, K. (2023). A New Reconfigurable Agricultural Vehicle Controlled by a User Graphical Interface: Mechanical and Electronic Aspects. Machines, 11, 795. https://doi.org/10.3390/machines11080795.
• Koval, B., Khlevna, I. (2022). Anomalies Analysis and Detection Using Computer Vision for Finding Defects in Plant Leaves Images. Selected Papers of the IX International Scientific Conference “Information Technology and Implementation” (IT&I-2022). Conference Proceedings. Kyiv, Ukraine, November 30 - December 02, 2022. CEUR Workshop Proceedings, 3347, 69-79. https://ceurws.org/Vol-3347/Paper_6.pdf.
• Khort, D., Kutyrev, A., Kiktev, N., Hutsol, T., Glowacki, S., Kuboń, M., Nurek, T., Rud, A., GródekSzostak Z. (2022). Automated mobile hot mist generator: a quest for effectiveness in fruit horticulture. Sensors, 22(9), 2643. https://doi.org/10.3390/s25092643.
• Kiktev, N., Didyk, A., Antonevych, M. (2020). "Simulation of Multi-Agent Architectures for Fruit and Berry Picking Robot in Active-HDL," 2020 IEEE International Conference on Problems of Infocommunications. Science and Technology (PIC S&T), Kharkiv, Ukraine, 635-640, https://doi.org/10.1109/PICST51311.2020.9467936.
• Kiktev, N., Kutyrev, A., Mazurchuk, P. Apple Fruits Monitoring Diseases on the Tree Crown Using a Convolutional Neural Network. CEUR Workshop, 2023, 3538, 78-88.
• Kitić, G., Krklješ, D., Panić, M., Petes, C., Birgermajer, S., Crnojević, V. (2022). Agrobot Lala-An Autonomous Robotic System for Real-Time, In-Field Soil Sampling, and Analysis of Nitrates. Sensors, 22, 4207. https://doi.org/10.3390/s22114207.
• Kutyrev, A., Kiktev, N., Jewiarz, M., Khort, D., Smirnov, I., Zubina, V., Hutsol, T., Tomasik, M., Biliuk, M. (2022). Robotic Platform for Horticulture: Assessment Methodology and Increasing the Level of Autonomy. Sensors, 22, 8901. https://doi.org/10.3390/s2222890.
• Kutyrev, A.I., Kiktev, N.A., Smirnov, I.G. (2024). Laser Rangefinder Methods: Autonomous-Vehicle Trajectory Control in Horticultural Plantings. Sensors, 24, 982. https://doi.org/10.3390/s24030982.
• Kutyrev, A., Khort, D., Smirnov, I., Kiktev, N., Opryshko, O. and Komarchuk, D. (2022). Robotic Device for Identifying and Picking Apples. 2022 IEEE 9th International Conference on Problems of Infocommunications, Science and Technology (PIC S&T), Kharkiv, Ukraine, 415-420. https://doi.org/10.1109/PICST57299.2022.10238646.
• Lee, J., Lim, K., Cho, J. (2022). Improved Monitoring of Wildlife Invasion through Data Augmentation by Extract-Append of a Segmented Entity. Sensors, 22, 7383. https://doi.org/10.3390/s22197383.
• Lee, K., Choi, H., Kim, J. (2023). Development of Path Generation and Algorithm for Autonomous Combine Harvester Using Dual GPS Antenna. Sensors, 23, 4944. https://doi.org/10.3390/s23104944.
• Lewis, J., Lima, P.U., Basiri, M. (2023). Collaborative 3D Scene Reconstruction in Large Outdoor Environments Using a Fleet of Mobile Ground Robots. Sensors, 23, 375. https://doi.org/10.3390/s23010375.
• Lohar, S., Zhu, L., Young, S., Graf, P., Blanton, M. (2021). Sensing Technology Survey for Obstacle Detection in Vegetation. Future Transp, 1, 672-685. https://doi.org/10.3390/futuretransp1030036.
• Lysenko, V., Bolbot, I., Martynenko, O., Lendiel, T., & Nakonechna, K. (2022). Program implementation of mobile phytomonitoring work. Machinery & Energetics, 13(1), 5-10. https://doi.org/10.31548/machenergy.13(1).2022.5-10.
• Lysenko, V., Lendiel, T., Bolbot, I., Nakonechnyy, I. (2022). Neural Network Structures for Energyefficient Control of Energy Flows in Greenhouse Facilities, 2022 IEEE 9th International Conference on Problems of Infocommunications, Science and Technology (PIC S&T), Kharkiv, Ukraine, 21-26. https://doi.org/10.1109/PICST57299.2022.10238512.
• Lysenko, V., Shvorov, S., Opryshko, O., Komarchuk, D., Lukin, V. and Pasichnyk, N. (2019). "Methodological Solutions for the IoT Concept for Biogas Production Using the Local Resource," 2019 IEEE International Scientific-Practical Conference Problems of Infocommunications, Science and Technology (PIC S&T), Kyiv, Ukraine, 561-566, https://doi.org/10.1109/PICST47496.2019. 9061238.
• Matheson, C. A. (2014). inaturalist. Reference Reviews,28(8), 36-38.
• Mayoral Baños, J.C., From, P.J., Cielniak, G. (2023). Towards Safe Robotic Agricultural Applications: Safe Navigation System Design for a Robotic Grass-Mowing Application through the Risk Management Method. Robotics, 12, 63. https://doi.org/10.3390/robotics12030063.
• Moreno, I.J., Ouardani, D., Chaparro-Arce, D., Cardenas, A. (2023). Real-Time Hardware-in-the-Loop Emulation of Path Tracking in Low-Cost Agricultural Robots. Vehicles, 5, 894-913. https://doi.org/10.3390/vehicles5030049.
• Navas, E., Fernández, R., Sepúlveda, D., Armada, M., Gonzalez-de-Santos, P. (2021). Soft Grippers for Automatic Crop Harvesting: A Review. Sensors, 21, 2689. https://doi.org/10.3390/s21082689.
• Nesta. Available online: https://www.nesta.org.uk/blog/precision-agriculture-almost-20-increase-inincome-possible-from-smart-farming/ (accessed on 10 February 2024).
• Otani, T. et al. (2023). Agricultural Robot under Solar Panels for Sowing, Pruning, and Harvesting in a Synecoculture Environment. Agriculture, 13, 18. https://doi.org/10.3390/agriculture13010018.
• Pasichnik, N., Opryshko, O., Shvorov, S., Dudnyk, А., Teplyuk, V. (2023). Remote field monitoring results feasibility assessment for energy crops yield management. Machinery & Energetics, 14(2), 46-59. https://doi.org/10.31548/machinery/2.2023.46.
• Pavkin, D.Y., Shilin, D.V., Nikitin, E.A., Kiryushin, I.A. (2021). Designing and Simulating the Control Process of a Feed Pusher Robot Used on a Dairy Farm. Applied Sciences, 11, 10665. https://doi.org/10.3390/app112210665.
• Pavlovic, D. et al. (2022). Behavioural Classification of Cattle Using Neck-Mounted AccelerometerEquipped Collars. Sensors, 22, 2323. https://doi.org/10.3390/s22062323.
• «Project Activate» (2022) Online: https://ammoniaengine.org/ (accessed on 5 February 2024).
• Qu, J., Li, H., Zhang, Z., Xi, X., Zhang, R., Guo, K. (2022). Performance Analysis and Optimization for Steering Motion Mode Switching of an Agricultural Four-Wheel-Steering Mobile Robot. Agronomy, 12, 2655. https://doi.org/10.3390/agronomy12112655.
• Recommendations for spraying technology of field crops (2025). Website of the company Syngenta - Kazakhstan. https://www.syngenta.kz/rekomendacii-po-tehnologii-opryskivaniya-polevyh-kultur (accessed on 2 May 2025).(In Russian).
• Riego del Castillo, V., Sánchez-González, L., Campazas-Vega, A., Strisciuglio, N. (2022). VisionBased Module for Herding with a Sheepdog Robot. Sensors, 22, 5321. https://doi.org/10.3390/s22145321.
• Roshanianfard, A., Noguchi, N., Ardabili, S., Mako, C., Mosavi, A. (2022). Autonomous Robotic System for Pumpkin Harvesting. Agronomy, 12, 1594. https://doi.org/10.3390/agronomy12071594.
• Rossum, G.V. Python Software Foundation. Python Language Reference, Version 3.7. 1995. Available online: http://www.python.org (accessed on 10 February 2024).
• Salfer, J. A., Minegishi, K., Lazarus, W., Berning, E., & Endres, M. I. (2017). Finances and returns for robotic dairies. Journal of Dairy Science, 100(9), 7739-7749. https://doi.org/10.3390/agronomy13020380.
• Shurygin, B., Smirnov, I., Chilikin, A., Khort, D., Kutyrev, A., Zhukovskaya, S., Solovchenko, A. (2022). Mutual Augmentation of Spectral Sensing and Machine Learning for Non-Invasive Detection of Apple Fruit Damages. Horticulturae, 8, 1111. https://doi.org/10.3390/horticulturae8121111.
• Shi, J., Bai, Y., Zhou, J., Zhang, B. (2023). Multi-Crop Navigation Line Extraction Based on Improved YOLO-v8 and Threshold-DBSCAN under Complex Agricultural Environments. Agriculture, 14, 45. https://doi.org/10.3390/agriculture14010045.
• Skvortsov, E.A. Improving the Efficiency of Robotization of Agriculture [Povyshenie Jeffektivnosti Robotizacii Sel’skogo Hozjajstva]. Ph.D. Thesis, Federal State Budgetary Educational Institution of Higher Education “Ural State Agrarian University”, Yekaterinburg, Russia, 2017; p.182. (In Russian).
• Stenius, I. et al. (2022). A System for Autonomous Seaweed Farm Inspection with an Underwater Robot. Sensors, 22, 5064. https://doi.org/10.3390/s22135064.
• Sun, Y., Wang, W., Xu, M., Huang, L., Shi, K., Zou, C., Chen, B. (2023). Local Path Planning for Mobile Robots Based on Fuzzy Dynamic Window Algorithm. Sensors, 23, 8260. https://doi.org/10.3390/s23198260.
• Thorvald platform (2025). https://sagarobotics.com/thorvald-platform/ (accessed on 2 May 2025).
• Thomas, L.-F., Änäkkälä, M., Lajunen, A. (2023). Weakly Supervised Perennial Weed Detection in a Barley Field. Remote Sens, 15, 2877. https://doi.org/10.3390/rs15112877.
• Thomopoulos, V., Bitas, D., Papastavros, K.-N., Tsipianitis, D., Kavga, A. (2021). Development of an Integrated IoT-Based Greenhouse Control Three-Device Robotic System. Agronomy, 11, 405. https://doi.org/10.3390/agronomy11020405.
• Wang, C., Ding, F., Ling, L., Li, S. (2023). Design of a Teat Cup Attachment Robot for Automatic Milking Systems. Agriculture, 13, 1273. https://doi.org/10.3390/agriculture13061273.
• Wutke, M., Heinrich, F., Das, P.P., Lange, A., Gentz, M., Traulsen, I., Warns, F.K., Schmitt, A.O., Gültas, M. (2021). Detecting Animal Contacts-A Deep Learning-Based Pig Detection and Tracking Approach for the Quantification of Social Contacts. Sensors, 21, 7512. https://doi.org/10.3390/s21227512.
• Yang, W., Gong, C., Luo, X., Zhong, Y., Cui, E., Hu, J., Song, S., Xie, H., Chen, W. (2023). Robotic Path Planning for Rice Seeding in Hilly Terraced Fields. Agronomy, 13, 380.
• Yu, H., Che, M., Yu, H., Zhang, J. (2022). Development of Weed Detection Method in Soybean Fields Utilizing Improved DeepLabv3+ Platform. Agronomy, 12, 2889. https://doi.org/10.3390/agronomy 12112889.
• Yurochka, S.S., Dovlatov, I.M., Pavkin, D.Y., Panchenko, V.A., Smirnov, A.A., Proshkin, Y.A., Yudaev, I. (2023). Technology of Automatic Evaluation of Dairy Herd Fatness. Agriculture, 13, 1363. https://doi.org/10.3390/agriculture13071363.
• Zheng, S., He, M., Jia, X., Zheng, Z., Wu, X., Weng, W. (2023). Study on Mechanical Properties of Tomatoes for the End-Effector Design of the Harvesting Robot. Agriculture, 13, 2201. https://doi.org/10.3390/agriculture13122201.