Springer Smart Agricultural Technologies for Climate Resilience: A Sustainable Agriculture 4.0 Framework

This book connects agri-food 4.0 technology with climate-smart agriculture (CSA) to mitigate the bidirectional relationship between the agricultural sector and global warming and the associated risks within the agri-food industry. Furthermore, traditional farming methods face threats from declining natural resources, including clean water and fertile soil, essential for food production. In light of this information, the integration of intelligent sensors and advanced technologies is transforming precision agriculture. This book provides insights into current research, focusing on combining smart sensors with the Internet of Things (IoT), big data analytics, and Artificial Intelligence (AI). The goal is to shift towards farming practices that are more sustainable and efficient. Traditional monitoring methods of Pest infestations and crop diseases are often slow and inefficient, highlighting the need for effective prediction strategies to enable early detection. In this regard, the book highlights a machine learning model for pest prediction that aligns predictions with agronomic knowledge, serving as a valuable tool for early warning systems and helping reduce crop losses. The chapter on Genomic prediction tools enhances crop breeding through statistical methods and discusses applying machine learning techniques to predict phenotypic traits based on genetic markers and environmental data. Challenges include limited high-quality datasets and inconsistent metadata. The key highlights of the book are the frameworks for practitioners in the agri-food supply chain, emphasizing the importance of supply chain visibility for building data analytics capabilities. Additionally, the goal is to enrich the audience with new advancements in soilless agriculture methods, such as hydroponics and aeroponics, discussing their advantages and the precision technologies applied. Finally, the book elaborates on the challenges of using multi-temporal remote sensing imagery for crop monitoring, mainly due to cloud cover, and introduces a novel strategy for integrating optical and Synthetic Aperture Radar (SAR) data.