IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v11y2021i6p497-d563390.html
   My bibliography  Save this article

Artificial Neural Networks in Agriculture

Author

Listed:
  • Sebastian Kujawa

    (Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
    These authors contributed equally to this work.)

  • Gniewko Niedbała

    (Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
    These authors contributed equally to this work.)

Abstract

Artificial neural networks are one of the most important elements of machine learning and artificial intelligence. They are inspired by the human brain structure and function as if they are based on interconnected nodes in which simple processing operations take place. The spectrum of neural networks application is very wide, and it also includes agriculture. Artificial neural networks are increasingly used by food producers at every stage of agricultural production and in efficient farm management. Examples of their applications include: forecasting of production effects in agriculture on the basis of a wide range of independent variables, verification of diseases and pests, intelligent weed control, and classification of the quality of harvested crops. Artificial intelligence methods support decision-making systems in agriculture, help optimize storage and transport processes, and make it possible to predict the costs incurred depending on the chosen direction of management. The inclusion of machine learning methods in the “life cycle of a farm” requires handling large amounts of data collected during the entire growing season and having the appropriate software. Currently, the visible development of precision farming and digital agriculture is causing more and more farms to turn to tools based on artificial intelligence. The purpose of this Special Issue was to publish high-quality research and review papers that cover the application of various types of artificial neural networks in solving relevant tasks and problems of widely defined agriculture.

Suggested Citation

  • Sebastian Kujawa & Gniewko Niedbała, 2021. "Artificial Neural Networks in Agriculture," Agriculture, MDPI, vol. 11(6), pages 1-6, May.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:6:p:497-:d:563390
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/11/6/497/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/11/6/497/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Héctor García-Martínez & Héctor Flores-Magdaleno & Roberto Ascencio-Hernández & Abdul Khalil-Gardezi & Leonardo Tijerina-Chávez & Oscar R. Mancilla-Villa & Mario A. Vázquez-Peña, 2020. "Corn Grain Yield Estimation from Vegetation Indices, Canopy Cover, Plant Density, and a Neural Network Using Multispectral and RGB Images Acquired with Unmanned Aerial Vehicles," Agriculture, MDPI, vol. 10(7), pages 1-24, July.
    2. Mingbang Zhu & Shanshan Liu & Ziqing Xia & Guangxing Wang & Yueming Hu & Zhenhua Liu, 2020. "Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN," Agriculture, MDPI, vol. 10(8), pages 1-16, August.
    3. Emerson Rodolfo Abraham & João Gilberto Mendes dos Reis & Oduvaldo Vendrametto & Pedro Luiz de Oliveira Costa Neto & Rodrigo Carlo Toloi & Aguinaldo Eduardo de Souza & Marcos de Oliveira Morais, 2020. "Time Series Prediction with Artificial Neural Networks: An Analysis Using Brazilian Soybean Production," Agriculture, MDPI, vol. 10(10), pages 1-18, October.
    4. Katarzyna Pentoś & Krzysztof Pieczarka & Kamil Serwata, 2021. "The Relationship between Soil Electrical Parameters and Compaction of Sandy Clay Loam Soil," Agriculture, MDPI, vol. 11(2), pages 1-11, February.
    5. Kanitta Yarak & Apichon Witayangkurn & Kunnaree Kritiyutanont & Chomchanok Arunplod & Ryosuke Shibasaki, 2021. "Oil Palm Tree Detection and Health Classification on High-Resolution Imagery Using Deep Learning," Agriculture, MDPI, vol. 11(2), pages 1-16, February.
    6. Yang Li & Xuewei Chao, 2020. "ANN-Based Continual Classification in Agriculture," Agriculture, MDPI, vol. 10(5), pages 1-15, May.
    7. Piotr Boniecki & Krzysztof Koszela & Krzysztof Świerczyński & Jacek Skwarcz & Maciej Zaborowicz & Jacek Przybył, 2020. "Neural Visual Detection of Grain Weevil ( Sitophilus granarius L.)," Agriculture, MDPI, vol. 10(1), pages 1-9, January.
    8. Gniewko Niedbała & Danuta Kurasiak-Popowska & Kinga Stuper-Szablewska & Jerzy Nawracała, 2020. "Application of Artificial Neural Networks to Analyze the Concentration of Ferulic Acid, Deoxynivalenol, and Nivalenol in Winter Wheat Grain," Agriculture, MDPI, vol. 10(4), pages 1-12, April.
    9. Mo Wang & Jing Wang & Li Chen, 2020. "Mapping Paddy Rice Using Weakly Supervised Long Short-Term Memory Network with Time Series Sentinel Optical and SAR Images," Agriculture, MDPI, vol. 10(10), pages 1-19, October.
    10. Galih Kusuma Aji & Kenji Hatou & Tetsuo Morimoto, 2020. "Modeling the Dynamic Response of Plant Growth to Root Zone Temperature in Hydroponic Chili Pepper Plant Using Neural Networks," Agriculture, MDPI, vol. 10(6), pages 1-14, June.
    11. Tavseef Mairaj Shah & Durga Prasad Babu Nasika & Ralf Otterpohl, 2021. "Plant and Weed Identifier Robot as an Agroecological Tool Using Artificial Neural Networks for Image Identification," Agriculture, MDPI, vol. 11(3), pages 1-31, March.
    12. Mohsen Niazian & Gniewko Niedbała, 2020. "Machine Learning for Plant Breeding and Biotechnology," Agriculture, MDPI, vol. 10(10), pages 1-23, September.
    13. Beata Cieniawska & Katarzyna Pentos, 2021. "Average Degree of Coverage and Coverage Unevenness Coefficient as Parameters for Spraying Quality Assessment," Agriculture, MDPI, vol. 11(2), pages 1-14, February.
    14. Dhivya Elavarasan & Durai Raj Vincent P M & Kathiravan Srinivasan & Chuan-Yu Chang, 2020. "A Hybrid CFS Filter and RF-RFE Wrapper-Based Feature Extraction for Enhanced Agricultural Crop Yield Prediction Modeling," Agriculture, MDPI, vol. 10(9), pages 1-27, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dominika Sieracka & Maciej Zaborowicz & Jakub Frankowski, 2023. "Identification of Characteristic Parameters in Seed Yielding of Selected Varieties of Industrial Hemp ( Cannabis sativa L.) Using Artificial Intelligence Methods," Agriculture, MDPI, vol. 13(5), pages 1-11, May.
    2. Campos, Jean C. & Manrique-Silupú, José & Dorneanu, Bogdan & Ipanaqué, William & Arellano-García, Harvey, 2022. "A smart decision framework for the prediction of thrips incidence in organic banana crops," Ecological Modelling, Elsevier, vol. 473(C).
    3. Awe, Olushina Olawale & Dias, Ronaldo, 2022. "Comparative Analysis of ARIMA and Artificial Neural Network Techniques for Forecasting Non-Stationary Agricultural Output Time Series," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 14(4), December.
    4. Shirin Ghatrehsamani & Gaurav Jha & Writuparna Dutta & Faezeh Molaei & Farshina Nazrul & Mathieu Fortin & Sangeeta Bansal & Udit Debangshi & Jasmine Neupane, 2023. "Artificial Intelligence Tools and Techniques to Combat Herbicide Resistant Weeds—A Review," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    5. Piotr Boniecki & Agnieszka Sujak & Gniewko Niedbała & Hanna Piekarska-Boniecka & Agnieszka Wawrzyniak & Andrzej Przybylak, 2023. "Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications," Agriculture, MDPI, vol. 13(4), pages 1-19, March.
    6. Sebastian C. Ibañez & Christopher P. Monterola, 2023. "A Global Forecasting Approach to Large-Scale Crop Production Prediction with Time Series Transformers," Agriculture, MDPI, vol. 13(9), pages 1-27, September.
    7. Bonfiglio, A. & Camaioni, B. & Carta, V. & Cristiano, S., 2023. "Estimating the common agricultural policy milestones and targets by neural networks," Evaluation and Program Planning, Elsevier, vol. 99(C).
    8. Chun-Ming Xu & Jia-Shuai Zhang & Ling-Qiang Kong & Xue-Bo Jin & Jian-Lei Kong & Yu-Ting Bai & Ting-Li Su & Hui-Jun Ma & Prasun Chakrabarti, 2022. "Prediction Model of Wastewater Pollutant Indicators Based on Combined Normalized Codec," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    9. Marek Gaworski & Piotr F. Borowski & Łukasz Kozioł, 2022. "Supporting Decision-Making in the Technical Equipment Selection Process by the Method of Contradictory Evaluations," Sustainability, MDPI, vol. 14(13), pages 1-17, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2022. "Prediction of Protein Content in Pea ( Pisum sativum L.) Seeds Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
    2. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2021. "Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data," Land, MDPI, vol. 10(6), pages 1-21, June.
    3. Xue-Bo Jin & Wen-Tao Gong & Jian-Lei Kong & Yu-Ting Bai & Ting-Li Su, 2022. "PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data," Mathematics, MDPI, vol. 10(4), pages 1-17, February.
    4. Piotr Boniecki & Maciej Zaborowicz & Agnieszka Pilarska & Hanna Piekarska-Boniecka, 2020. "Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN," Agriculture, MDPI, vol. 10(6), pages 1-9, June.
    5. Alper Taner & Yeşim Benal Öztekin & Hüseyin Duran, 2021. "Performance Analysis of Deep Learning CNN Models for Variety Classification in Hazelnut," Sustainability, MDPI, vol. 13(12), pages 1-13, June.
    6. Bożena Kordan & Mariusz Nietupski & Emilia Ludwiczak & Beata Gabryś & Robert Cabaj, 2023. "Selected Cultivar-Specific Parameters of Wheat Grain as Factors Influencing Intensity of Development of Grain Weevil Sitophilus granarius (L.)," Agriculture, MDPI, vol. 13(8), pages 1-13, July.
    7. Gabriel Italo Novaes da Silva & Alexandre Maniçoba da Rosa Ferraz Jardim & Wagner Martins dos Santos & Alan Cézar Bezerra & Elisiane Alba & Marcos Vinícius da Silva & Jhon Lennon Bezerra da Silva & Lu, 2024. "Estimation of Biophysical Parameters of Forage Cactus Under Different Agricultural Systems Through Vegetation Indices and Machine Learning Using RGB Images Acquired with Unmanned Aerial Vehicles," Agriculture, MDPI, vol. 14(12), pages 1-16, November.
    8. Quan Xu & Mengting Jin & Peng Guo, 2022. "A High-Precision Crop Classification Method Based on Time-Series UAV Images," Agriculture, MDPI, vol. 13(1), pages 1-18, December.
    9. Andrzej Przybylak & Radosław Kozłowski & Ewa Osuch & Andrzej Osuch & Piotr Rybacki & Przemysław Przygodziński, 2020. "Quality Evaluation of Potato Tubers Using Neural Image Analysis Method," Agriculture, MDPI, vol. 10(4), pages 1-11, April.
    10. Marley Nunes Vituri Toloi & Silvia Helena Bonilla & Rodrigo Carlo Toloi & Helton Raimundo Oliveira Silva & Irenilza de Alencar Nääs, 2021. "Development Indicators and Soybean Production in Brazil," Agriculture, MDPI, vol. 11(11), pages 1-15, November.
    11. Gniewko Niedbała & Danuta Kurasiak-Popowska & Magdalena Piekutowska & Tomasz Wojciechowski & Michał Kwiatek & Jerzy Nawracała, 2022. "Application of Artificial Neural Network Sensitivity Analysis to Identify Key Determinants of Harvesting Date and Yield of Soybean ( Glycine max [L.] Merrill) Cultivar Augusta," Agriculture, MDPI, vol. 12(6), pages 1-17, May.
    12. Tan Wang & Xianbao Xu & Cong Wang & Zhen Li & Daoliang Li, 2021. "From Smart Farming towards Unmanned Farms: A New Mode of Agricultural Production," Agriculture, MDPI, vol. 11(2), pages 1-26, February.
    13. Juan Diego Valenzuela-Cobos & Fabricio Guevara-Viejó & Purificación Vicente-Galindo & Purificación Galindo-Villardón, 2023. "Eco-Friendly Biocontrol of Moniliasis in Ecuadorian Cocoa Using Biplot Techniques," Sustainability, MDPI, vol. 15(5), pages 1-12, February.
    14. Priya Brata Bhoi & Veeresh S. Wali & Deepak Kumar Swain & Kalpana Sharma & Akash Kumar Bhoi & Manlio Bacco & Paolo Barsocchi, 2021. "Input Use Efficiency Management for Paddy Production Systems in India: A Machine Learning Approach," Agriculture, MDPI, vol. 11(9), pages 1-27, August.
    15. Katarzyna Szwedziak & Ewa Polańczyk & Żaneta Grzywacz & Gniewko Niedbała & Wiktoria Wojtkiewicz, 2020. "Neural Modeling of the Distribution of Protein, Water and Gluten in Wheat Grains during Storage," Sustainability, MDPI, vol. 12(12), pages 1-14, June.
    16. Quanfeng Li & Wenhao Guo & Xiaobing Sun & Aizheng Yang & Shijin Qu & Wenfeng Chi, 2021. "The Differentiation in Cultivated Land Quality between Modern Agricultural Areas and Traditional Agricultural Areas: Evidence from Northeast China," Land, MDPI, vol. 10(8), pages 1-15, August.
    17. Mohammad Fatin Fatihur Rahman & Shurui Fan & Yan Zhang & Lei Chen, 2021. "A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture," Agriculture, MDPI, vol. 11(1), pages 1-26, January.
    18. Awe, Olushina Olawale & Dias, Ronaldo, 2022. "Comparative Analysis of ARIMA and Artificial Neural Network Techniques for Forecasting Non-Stationary Agricultural Output Time Series," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 14(4), December.
    19. Chunling Sun & Hong Zhang & Lu Xu & Chao Wang & Liutong Li, 2021. "Rice Mapping Using a BiLSTM-Attention Model from Multitemporal Sentinel-1 Data," Agriculture, MDPI, vol. 11(10), pages 1-20, October.
    20. Romeu Gerardo & Isabel P. de Lima, 2023. "Applying RGB-Based Vegetation Indices Obtained from UAS Imagery for Monitoring the Rice Crop at the Field Scale: A Case Study in Portugal," Agriculture, MDPI, vol. 13(10), pages 1-18, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:11:y:2021:i:6:p:497-:d:563390. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.