TinyML and IoT in Agriculture

The convergence of Tiny Machine Learning with the Internet of Things in agriculture brings real-time precision, enhanced autonomy, and sustainable practices-all under one umbrella. This chapter looks at how innovation through this integration can solve the major problems of smart farming with new methods. It's predicted by an adaptive drip irrigation system that utilizes TinyML sensors to gauge rainfall and adapt dynamic delivery of water to use the resources efficaciously as well as improve crop health. Smart fertilization methods utilize real-time soil nutrient analysis, crop nutrient needs, and environmental data to optimize the application of fertilizers so that production enhancement is accomplished while the environmental cost is minimized. Farm security is achieved via an animal intrusion detection system, using TinyML-enabled sensors that can sense unwanted animal presence and hence inform farmers of its existence, thus preventing potential losses from it. This entails real-time monitoring of the climatic conditions for optimal growth of plants with temperature-controlled greenhouse management systems. Some of these technologies use crop protection, such as early disease detection and weed identification, with TinyML algorithms that work on image and environmental data. This technology makes an early forecast of disease or weed so that yield losses do not occur and there is no waste of resources. This chapter shows the way in which TinyML and IoT are combined to create a data-driven, resource-efficient framework for better and easier agricultural activity. The technologies will help to improve productivity, enhance resilience against environmental threats, and promote sustainable farming practices toward smarter and more adaptable agricultural systems.