Titlebook: Smart Big Data in Digital Agriculture Applications; Acquisition, Advance Haoyu Niu,YangQuan Chen Book 2024 The Editor(s) (if applicable) an

滑稽

978-3-031-52647-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

Suppository

令人作呕

Haoyu Niu,YangQuan ChenSummarizes the state-of-the-art agriculture applications with small UAV.Highlights new field methods for data gathering with machine learning.Special focuses on smart data acquisition and analysis

Devastate

Fundamentals of Big Data, Machine Learning, and Computer Vision Workflowbegins with an introduction, setting the stage for a comprehensive tutorial that elucidates the workflow’s fundamentals. The chapter unfolds with a step-by-step tutorial focused on the classification of cotton water stress using Convolutional Neural Networks (CNNs).

完成

Introduction book’s structure and the topics covered in each section. This comprehensive introduction serves as a foundation for the readers to grasp the significance of plant physiology-informed artificial intelligence in the realm of digital agriculture, providing a compelling rationale for the subsequent cha

大厅

Angiogenesis

bleach

The Edge-AI Sensors and Internet of Living Things (IoLT)on treatment inference using a low-cost Edge-AI sensor, offering insights into the potential for efficient and cost-effective digital agriculture practices. The case study includes a thorough exploration of materials, methods, results, and discussion, culminating in conclusions and suggestions for f

植物群

The Unmanned Ground Vehicles (UGVs) for Digital Agriculturee. The study provides insights into the practical implementation of UGVs in the agricultural domain, emphasizing the feasibility of adopting cost-effective solutions for digital agriculture. The chapter concludes with a comprehensive summary, encapsulating key insights into the role of UGVs in digit

FRET

A Low-Cost Proximate Sensing Method for Early Detection of Nematodes in Walnut Using Machine Learninmance of classifiers in both Project 45 instances in 2019 and 2020. The findings offer insights into the effectiveness of the low-cost proximate sensing method for early nematode detection, providing a foundation for further exploration and refinement. The chapter concludes with a comprehensive summ