Journal of Advances in Biology & Biotechnology

Artificial intelligence (AI) is reshaping crop protection by converting images, sensor streams and environmental observations into predictions or actions that can support disease diagnosis, pest surveillance, weed management and site-specific interventions. Yet the maturity of these applications is uneven. This critical narrative review evaluates the evidence for AI across the crop-protection pathway rather than treating high classification accuracy as equivalent to agronomic effectiveness. Literature published principally from 2010 to 10 June 2026 was examined, with earlier seminal work retained where conceptually necessary. The synthesis integrates computer vision, machine learning, deep learning, remote and proximal sensing, automated traps, decision-support modelling and precision application systems, while critically appraising external validation, deployment constraints and evidence of chemical-input or crop-loss reduction. The strongest evidence concerns recognition tasks under bounded conditions, including leaf-image classification, weed segmentation and automated insect counting. Field studies demonstrate that mobile imaging, unmanned aerial vehicles, multispectral or hyperspectral sensing and smart traps can extend observation beyond laboratory datasets, but performance commonly declines when illumination, cultivar, growth stage, background, device, geography or pest density differs from the training domain. Forecasting systems based on weather and temporal data show promise for moving from diagnosis to anticipation, although many remain locally calibrated and have limited prospective validation. A central weakness across the literature is the translation gap between model-level metrics and protection outcomes such as avoided crop loss, reduced pesticide use, treatment timing, non-target effects, labour requirements and farm profitability. The review argues that the next phase of AI-enabled crop protection should prioritise multi-site prospective trials, uncertainty-aware and cost-sensitive decisions, multimodal sensing, open and representative datasets, interoperable edge systems, and integration with integrated pest management rather than autonomous chemical intensification. AI is therefore best understood not as a replacement for crop-protection expertise, but as an enabling layer whose value depends on ecological context, agronomic thresholds and reliable sensing-to-action design.