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

作     者：Castillo-Girones, Salvador Munera, Sandra Martinez-Sober, Marcelino Blasco, Jose Cubero, Sergio Gomez-Sanchis, Juan 

作者机构：Inst Valenciano Invest Agr IVIA Ctr Agroingn CV-315Km 107 Moncada 46113 Valencia Spain Univ Politecn Valencia Dept Ingn Graf Camino Vera S-N Valencia 46022 Spain Univ Valencia Dept Ingn Elect IDAL Ave Univ S-N Burjassot 46100 Valencia Spain 

出 版 物：《COMPUTERS AND ELECTRONICS IN AGRICULTURE》 (Comput. Electron. Agric.)

年 卷 期：2025年第230卷

核心收录：

学科分类：09[农学] 0901[农学-作物学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：FEDER funds [MICIU AEI PID2023-150192OR-C31, C-33] INIA for the FPI-INIA grant [AEI TED2021-130117B-C31, GVA-PROMETEO CIPROM/2021/014] Euro-pean Union FSE funds [PRE2020-094491] European Union NextGenerationEU/PRTR [FJC2021-047786-I, MCIN/AEI/10.13039/501100011033] 

主　　题：Deep Learning Neural Networks Applications Agricultural Products Quality 

摘      要：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 applications.