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

Abstract

Artificial Neural Networks (ANNs) based models have emerged as a powerful tool for solving complex nonlinear problems in agriculture. These models simulate the human nervous system’s structure, allowing them to learn hierarchical features from the data and solve nonlinear problems efficiently. Despite requiring a large amount of training data, ANNs with shallow architectures demonstrate superior performance in extracting relevant features and establishing accurate models, instilling confidence in their effectiveness compared to conventional machine learning methods. The versatility of ANNs enables their application in various agricultural domains, including precision agriculture, species classification, phenotyping, and food quality and safety assessment. ANNs combined with image analysis have proven valuable in disease detection, plant phenotyping, and fruit quality evaluation. The use of deep learning in agriculture has experienced exponential growth, as evident from the increasing number of publications in recent years. This article overviews recent advancements in applying ANNs in agriculture. It delves into the fundamental principles behind various types of agricultural data and ANN models, discussing their benefits and challenges. The article offers valuable insights into the proper use and functioning of each neural network, data processing for improved model outcomes, and the diverse applications of ANNs in the agricultural sector. It aims to equip readers with practical information on data utilisation, model selection based on data type, functionality, and current research application