The concept of precision agriculture (PA) refers to the observation, impact assessment, and timely strategic responses to fine-scale variations in the causal components of agricultural production. Big data and machine learning (ML) play integral roles in this paradigm by providing detailed insights into various farming processes. In PA, data from remote sensing platforms and weather forecasts are collected and analysed to monitor crop health and soil conditions, enabling farmers to make informed decisions. Examples of ML techniques and potential sources of big data are discussed in this chapter. Furthermore, three case studies examine the potential of big data and ML models within the PA cropping systems. The three case studies illustrated the practical integration of ML in processing this data, leading to enhanced decision-making. With the advancement of data collection techniques and machine learning methods, agricultural decision-making and overall productivity can be improved significantly.

Agriculture has always faced global challenges to meet the growing food needs, leading to the shrinking of arable lands. Efficient solutions to these real-world problems are hidden in developing high-yielding crop varieties and innovative precision agriculture technologies. Variable rate technologies (VRTs) have initiated a new trend in precision farming, allowing for the cost-effective and optimal application of water, pesticides and nutrients considering spatial and temporal variations. Through case studies and synthesis of scientific literature, the chapter presents VRTs and examines potential areas for future research and development. The chapter concludes that upcoming technological advancements have enormous potential to catalyse significant progress in crop protection, emphasizing improved production, sustainability and smart solutions to evolving global challenges around food security. The ongoing efforts to advance the precision, robustness and accessibility of VRT systems across various fields and applications are discussed.

Variable rate seeding (VRS) continues to advance in precision agriculture, allowing farmers to fine-tune seeding rates and depth across fields based on inherent spatio-temporal variability. Recent developments in VRS technology have been driven by advanced data analytics, sensor technology, and machine learning (ML) algorithms. These innovations enable real-time adjustments to both seeding rates and depths, enhancing crop yields and resource efficiency. Advanced GNSS and remote sensing technologies provide detailed field maps, while soil sensors and drones deliver precise data on soil variations, health assessments, and moisture levels. Machine learning (ML) models process this data to determine optimal seeding rates and depths for various field zones. These advances not only boost agricultural productivity but also support sustainable farming practices by minimizing input waste and environmental impact. As VRS technology continues to evolve, it promises to play a crucial role in meeting the growing global food demand while promoting environmental stewardship.

In agriculture, SS nutrient management systems have emerged as pivotal tools for enhancing productivity and sustainability. These systems, grounded in the principles of precision agriculture, aim to tailor nutrient application to the unique characteristics of individual field zones, ensuring optimal resource use and profit maximization while mitigating environmental impacts. The adoption of SS nutrient management represents a departure from traditional uniform application methods, trying to address the spatial and temporal variability inherent in landscapes. By integrating advanced technologies such as crop sensors, models, and machine learning, these systems provide farmers with the tools to make data-driven decisions that balance economic gains with environmental stewardship. This chapter explores the advancements, challenges, and applications of SS nutrient management. It delves into their economic considerations, technological developments, and potential to impact agriculture globally. The goal is to present an overview of how these systems can support sustainable and profitable farming in diverse systems.

This chapter reviews recent developments in crop sprayer technology. It adds to the content in a previous chapter in Precision agriculture for sustainability edited by John Stafford and published in 2019. This chapter particularly considers nozzle control on a boom, spray drift control, methods of maintaining nozzle height, and sprayer loading systems.