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Neural Networks for Smart Agriculture

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Encyclopedia of Digital Agricultural Technologies

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Definition

Neural networks, or Artificial Neural Networks (ANNs) in general sense, are one of artificial intelligence realizations, whose central theory is derived from the dynamic response of biological nerves to external inputs. A neural network is constructed by a collection of connected nodes called artificial neurons, which imitate the function of neurons in a biological brain.

Artificial neurons, in simple terms, are the building blocks of the ANNs and are connected layer-by-layer in a unique structure with a certain interconnection and density to replicate or imitate the behavior of human brain. Based on this unique structural composition, neural networks could learn and understand complex concepts or patterns from processed data by changing the strength of connection between neurons.

Neuron Models

Neural networks are computational structures that reflect certain properties of human brain, which are inspired by modern biological research basis and have become one of the most...

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References

  • Abdalla A, Cen H, Wan L, Mehmood K, He Y (2021) Nutrient status diagnosis of infield oilseed rape via deep learning-enabled dynamic model. IEEE Trans Ind Informatics 17:4379–4389. https://doi.org/10.1109/TII.2020.3009736

    Article  Google Scholar 

  • Chen J, Chen J, Zhang D, Sun Y, Nanehkaran YA (2020) Using deep transfer learning for image-based plant disease identification. Comput Electron Agric 173:105393

    Article  Google Scholar 

  • Gao F, Fu L, Zhang X, Majeed Y, Li R, Karkee M, Zhang Q (2020) Multi-class fruit-on-plant detection for apple in SNAP system using Faster R-CNN. Comput Electron Agric 176:105634

    Article  Google Scholar 

  • Kashyap PK, Kumar S, Jaiswal A, Prasad M, Gandomi AH (2021) Towards precision agriculture: IoT-enabled intelligent irrigation systems using deep learning neural network. IEEE Sensors J 21:17479–17491

    Article  Google Scholar 

  • Li G, Fu L, Gao C, Fang W, Zhao G, Shi F, Dhupia J, Zhao K, Li R, Cui Y (2022) Multi-class detection of kiwifruit flower and its distribution identification in orchard based on YOLOv5l and Euclidean distance. Comput Electron Agric 201:107342

    Article  Google Scholar 

  • McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133

    Article  Google Scholar 

  • Nasirahmadi A, Sturm B, Edwards S, Jeppsson KH, Olsson AC, Müller S, Hensel O (2019) Deep learning and machine vision approaches for posture detection of individual pigs. Sensors (Switzerland) 19(17):3738

    Article  Google Scholar 

  • Nunes L, Ampatzidis Y, Costa L, Wallau M (2021) Horse foraging behavior detection using sound recognition techniques and artificial intelligence. Comput Electron Agric 183:106080

    Article  Google Scholar 

  • Sun X, Fang W, Gao C, Fu L, Majeed Y, Liu X, Gao F, Yang R, Li R (2022) Remote estimation of grafted apple tree trunk diameter in modern orchard with RGB and point cloud based on SOLOv2. Comput Electron Agric 199:107209

    Article  Google Scholar 

  • Suo R, Fu L, He L, Li G, Majeed Y, Liu X, Zhao G, Yang R, Li R (2022) A novel labeling strategy to improve apple seedling segmentation using BlendMask for online grading. Comput Electron Agric 201:107333

    Article  Google Scholar 

  • Wiesner-Hanks T, Wu H, Stewart E, DeChant C, Kaczmar N, Lipson H, Gore MA, Nelson RJ (2019) Millimeter-level plant disease detection from aerial photographs via deep learning and crowdsourced data. Front Plant Sci 10:1550

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhao S, Liu J, Wu S (2022) Multiple disease detection method for greenhouse-cultivated strawberry based on multiscale feature fusion Faster R_CNN. Comput Electron Agric 199:107176

    Article  Google Scholar 

  • Zhou J, Zhou J, Ye H, Ali ML, Chen P, Nguyen HT (2021) Yield estimation of soybean breeding lines under drought stress using unmanned aerial vehicle-based imagery and convolutional neural network. Biosyst Eng 204:90–103

    Article  CAS  Google Scholar 

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Author information

Authors and Affiliations

  1. College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, China

    Longsheng Fu & Leilei He

  2. Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi, China

    Longsheng Fu

  3. Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi, China

    Longsheng Fu

  4. Northwest A&F University Shenzhen Research Institute, Shenzhen, Guangdong, China

    Longsheng Fu

  5. Center for Precision and Automated Agricultural Systems, Washington State University, Prosser, WA, USA

    Qin Zhang

Authors
  1. Longsheng Fu
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  2. Leilei He
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  3. Qin Zhang
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Corresponding author

Correspondence to Longsheng Fu .

Editor information

Editors and Affiliations

  1. Biological Systems Engineering, Washington State University, Prosser, WA, USA

    Qin Zhang

Section Editor information

  1. Biol. and Agricultural Engineering, UC Davis, Davis, USA

    Irwin R. Donis-Gonzalez

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Fu, L., He, L., Zhang, Q. (2023). Neural Networks for Smart Agriculture. In: Zhang, Q. (eds) Encyclopedia of Digital Agricultural Technologies. Springer, Cham. https://doi.org/10.1007/978-3-031-24861-0_164

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