Al-based Techniques for Comprehensive Crop Nutrition Assessment: A Review
Preeti Parihar
University Institute of Agriculture Sciences, Chandigarh University, India.
Simran Tiwary
University Institute of Agriculture Sciences, Chandigarh University, India.
Pooja Ranee Behuria
University Institute of Agriculture Sciences, Chandigarh University, India.
Yachna Sood
*
University Institute of Agriculture Sciences, Chandigarh University, India.
*Author to whom correspondence should be addressed.
Abstract
Artificial Intelligence (AI) methodologies, such as machine learning, deep learning, and data fusion, are transforming agricultural practices by offering advanced tools for data-driven decision-making, predictive modeling, and automated processes. This paper explores the latest AI techniques utilized in agriculture to enhance productivity, optimize resource management, and address the challenges posed by climate change. By harnessing AI-driven solutions for crop management, soil analysis, and pest control, these methods significantly contribute to sustainable agriculture, benefiting both farmers and the environment through more efficient and eco-friendly practices. However, while AI holds immense promise, challenges remain in terms of accessibility, data quality, and the adaptation of these techniques to diverse agricultural conditions. This review aims to provide a balanced overview of the current state of AI applications in agriculture, offering insights into the opportunities and limitations faced by this growing field.
Keywords: Advanced agricultural technologies, artificial intelligence, crop nutrition management, data fusion, IOT devices.