Revolutionizing agriculture: A comprehensive review on artificial intelligence applications in enhancing properties of agricultural produce

Integrating Artificial Intelligence (AI) in agriculture marks a new era of precision and efficiency. Convolutional Neural Networks (CNNs) enable early crop disease detection through image-based classification, reducing yield loss. Long Short-Term Memory (LSTM) networks support predictive modelling for yield forecasting and soil health assessment, aiding resource allocation. While mechanization and automation remain global challenges, modern AI and machine learning (ML) applications have transformed agricultural practices. This review explores various AI tools, including ML algorithms, deep learning (DL) models, Internet of Things (IoT), and Decision Support Systems (DSS), and their role in addressing challenges like maximizing crop yield, precision irrigation, pest control, and informed decision-making. The paper further highlights AI applications in plant breeding, irrigation, logistics, and packaging. Despite the advancements, widespread adoption faces barriers such as high costs, privacy concerns, inadequate infrastructure, and limited technical knowledge. The review offers insights into both the potential and limitations of AI in agriculture.