AI and Machine Learning Applications in Smart Agriculture: A Comprehensive Review

Abstract

Agriculture is undergoing a significant technological change through the use of artificial intelligence (AI) systems and machine learning (ML) techniques. The global demand for food security, sustainable practices, and environmental protection has created a need for intelligent systems that can produce the highest yields with the fewest resources. Machine learning has, over the last 10 years, become a major component of various agricultural fields, including yield forecasting, disease identification, irrigation management, and soil quality assessment.  Various supervised and unsupervised learning methods, including convolutional neural networks (CNNs), long short-term memory (LSTMs), support vector machines (SVMs), and random forests, have been used by scientists, yielding prediction accuracies far beyond those of traditional statistical approaches. Deep learning, especially hybrid CNN–LSTM architectures, is very promising for detecting crop stress, nutrient deficiency, and yield variation, and can even be done in real time. The current review echoes the findings of 40 landmark research works that trace precision agriculture through machine learning from 2021 to 2025 and examine how it has contributed to the development of sustainable farming methods. Besides, it substantiates the existence of fixed obstacles, such as data heterogeneity, scalability, and ethical data governance; it also pinpoints new and forthcoming research on AI-IoT convergence and multi-sensor fusion frameworks aimed at generating resilient, resource-efficient agricultural ecosystems.