Search

Assessing the extent of plant injury is a vital element of crop safeguarding. Currently, domain experts evaluate the plots by hand. This labor-intensive activity can be mechanized utilizing cutting-edge computer vision (CV) technology. Image-reliant systems, and lately those grounded in deep learning, have demonstrated strong performance across multiple agricultural use cases. These image-based applications surpass specialist appraisals in regulated conditions and are now progressively being incorporated into uncontrolled, in-field deployments. A new solution, rooted in deep learning techniques and image processing, is proposed to estimate crop damage under field conditions. The recommended solution operates through a two-stage pipeline. The first stage identifies individual plants within the plots using a YOLO-based object detection model. After that, a regression model is applied to gauge the damage per isolated plant. The system was designed and corroborated on oilseed rape plants to measure the damage inflicted by the flea beetle. The plant detection model achieves an overall mean average precision of 91%, with [email protected] of 0.99 and [email protected] of 0.91 for the oilseed rape category. The regression model, built to predict damage severity up to 60% in single plants, yields an MAE of 7.11 and an R² of 0.46 when evaluated against plant-by-plant assessments conducted by human experts. Both models are packaged in a Docker container and can be directly queried via a REST API using images captured in the field from a mobile device.