Predictive analysis techniques for crop and soil sensors

Abstract

Predictive analysis techniques for crop and soil sensors have revolutionized agriculture by enabling data-driven decision-making and optimizing farming practices. This chapter begins with an overview of crop and soil stresses, highlighting their impact on agricultural productivity. It then examines enabling technologies, such as sensors for soil and crop health monitoring, Geographic Information System (GIS)-based farm mapping for spatial variability management, and drones equipped with imaging systems for stress detection. Predictive analysis techniques, including machine learning, time-series forecasting, image analysis, and geostatistical methods, are discussed in detail, focusing on their applications in stress prediction and management. Internet of Things (IoT) integration and edge computing enable real-time data processing, supporting immediate insights. These techniques drive productivity, sustainability, and resilience in agriculture, addressing evolving challenges effectively. This chapter will cover various crop and soil sensors used in agriculture and data generated by these sensors, followed by a discussion on multiple analysis techniques for predicting future conditions, enhancing decision-making, and improving agriculture outcomes.