Artificial Neural Networks in Agriculture, the core of artificial intelligence: What, When, and Why

• Deep learning in agriculture is rapidly growing, with CNNs leading image analysis and Capsule Networks showing promise. • People often apply ANNs without considering the specific requirements of their data. Proper data preprocessing is crucial for optimizing ANN performance. • Different ANN types are better suited for specific agricultural data and tasks. • Misuse of ANNs, including incorrect hyperparameters and terminology, can lead to poor performance. • Feature engineering and simpler models should not be overlooked in ANN applications. Artificial Neural Networks (ANNs) based models have emerged as a powerful tool for solving complex nonlinear problems in agriculture. These models simulate the human…

• IN
  Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria
• EC
  European Commission
• GV
  Generalitat Valenciana

  Award: GVA-PROMETEO CIPROM/2021/014
• MD
  Ministerio de Ciencia e Innovación
• AE
  Agencia Estatal de Investigación

  Awards: 13039, AEI/10