Loading...
Sanjeev Bhardwaj is an Assistant Professor in the Department of Computer Science and Engineering, School of Computer Science & Applications, at IFTM University, Moradabad, India. His academic interests include computer science, Artificial Intelligence, and emerging intelligent technologies. He contributes to the application of Agentic AI for precision agriculture and autonomous farm management.
Precision agriculture is revolutionising traditional farming with the use of artificial intelligence, Internet of Things (IoT) technology, remote sensing, drones, robotics, cloud computing, and data-centric decision-making. The advent of Agentic Artificial Intelligence (Agentic AI) enhances these functionalities by allowing intelligent systems to independently observe agricultural settings, assess diverse data, contemplate farming goals, devise action strategies, engage with agricultural instruments and devices, and perpetually modify decisions in response to fluctuating field circumstances. This section examines the utilisation of Agentic AI in precision farming and self-sufficient agricultural management. It examines the amalgamation of soil sensors, meteorological stations, satellite imaging, UAV remote sensing, crop surveillance systems, agricultural machinery, irrigation systems, and farm management platforms. The section explores proactive strategies for selecting crops, managing soil and nutrients, implementing precision irrigation, controlling pests and diseases, monitoring crop growth, predicting yields, managing weeds, optimising farm resources, and utilising autonomous agricultural machinery.Agentic AI has the potential to establish a basis for more flexible, resource-efficient, data-informed, and sustainable agricultural practices, all while ensuring adequate farmer supervision and operational limitations.
The swift advancement of Artificial Intelligence (AI) has established novel frameworks for constructing intelligent systems that can comprehend intricate knowledge, produce significant content, and execute independent activities. Generative AI empowers computers to create text, images, music, video, and organised data, but Agentic AI enhances these functions by permitting AI systems to strategise, analyse, make choices, engage with external tools, and do complex activities aimed at defined goals. This chapter introduces a cohesive structure for the creation of multimodal generative and agentic AI systems applicable to real-world scenarios. The suggested framework includes the collection of multimodal data, extraction of modality-specific features, learning of shared representations, management of memory, and optimisation based on feedback. It also examines retrieval-augmented generation, multi-agent cooperation, planning frameworks, ethical AI, assessment methodologies, and implementation factors.