The field of precision agriculture is moving towards the integration of artificial intelligence and machine learning to improve crop disease management and yields. Recent developments have focused on the use of vision-language models, convolutional neural networks, and hybrid machine learning frameworks to automate crop disease detection and classification. These innovations have shown significant potential in enhancing the accuracy and efficiency of disease diagnosis, treatment recommendation, and crop selection. Noteworthy papers in this area include the proposal of a domain-aware framework for agricultural image processing, which improved diagnostic accuracy and symptom analysis for maize leaf disease identification. Another notable work is the development of a hybrid recommendation engine that integrates agronomic and economic forecasting to provide data-driven, economically optimized crop selection recommendations. Additionally, the introduction of a lightweight machine learning-based approach for poultry disease detection from fecal images has demonstrated a cost-effective and scalable alternative to deep learning models.