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Application of Artificial Neural Network in Agriculture

  • Raymond Sheriff* (Editor)
  • , Chiew Foong Kwong (Editor)
  • *Corresponding author for this work
  • University of Nottingham Ningbo China

Research output: Book/ReportBookpeer-review

24 Downloads (Pure)

Abstract

With the need for farming to become more efficient and environmentally sustainable to meet the demands of a growing global population, the application of artificial neural networks (ANNs) to inform and enhance agricultural practice and production is gathering momentum and interest.

This Reprint titled "Application of Artificial Neural Network in Agriculture" showcases fourteen high-quality research articles highlighting the use of ANNs and related technologies for pest detection, plant disease detection, nutritional monitoring and prediction, the classification of the phenology of beans, and the optimisation of egg production. We also present how ANNs may be used for predicting the power requirements of agricultural machinery and for dynamic behaviour forecasting of an indirect solar dryer.

The multidisciplinary nature of the articles presented will likely strongly appeal to researchers and scientists working in the areas of agricultural technology, communications engineering, computer science, data analytics, electronic engineering, information technology, image processing, and mathematical modelling.
Original languageEnglish
Place of PublicationSwitzerland
PublisherMDPI AG
Number of pages242
ISBN (Electronic)978-3-7258-3121-0
ISBN (Print)978-3-7258-3122-7
Publication statusPublished - 14 Feb 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 2 - Zero Hunger
    SDG 2 Zero Hunger
  2. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure
  3. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities
  4. SDG 12 - Responsible Consumption and Production
    SDG 12 Responsible Consumption and Production

Keywords

  • convolutional neural network
  • ResNet-50
  • Pest Detection
  • Plant Disease
  • Machine learning
  • Precision Agriculture
  • artificial neural network

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