<p>Every country’s ability to grow economically primarily depends on its agricultural production. Rapid population expansion, frequent climate change, and resource shortages make it difficult to meet the food needs of the current population. Precision agriculture has rapidly evolved with the integration of deep learning (DL) techniques, addressing key challenges such as crop selection, yield prediction, disease detection, pest management, and weed control. This systematic review provides a comprehensive analysis of recent advances in DL applications for agriculture, highlighting methodologies, models, and their impact on farm efficiency and sustainability. By reviewing 117 studies, we evaluate how DL models, including convolution neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), Transformers, and other pre-trained models, are employed in tasks ranging from image-based disease identification to time series crop yield forecasting. Recent developments in large vision models (LVMs) and the rise of multi-models further enhance precision agriculture by automating complex tasks such as disease detection and crop health monitoring. These approaches address challenges like limited labeled data and dataset imbalances, providing scalable and reliable solutions. Despite their potential, DL models face challenges such as computational resource demands, generalization difficulties in diverse environments, and real-time scalability. Future research should prioritize hybrid DL models, integration with internet of things (IoT) systems, and transfer learning to overcome these limitations. Additionally, incorporating LVMs and multi-models with advanced data fusion techniques can further improve precision agriculture practices, enabling real-time decision-making, adaptability, and sustainability. This review offers insights into the current state and future directions of DL in precision agriculture, emphasizing its transformative potential in modern agriculture.</p>