Improving accuracy and efficiency in plant detection on a novel, benchmarking real-world dataset

Detecting plants in images is central in precision agriculture, but can be challenging due to their small size, similarities in appearance, varying lighting and environmental conditions. Moreover, computational capacity in real-world settings may be limited. This work examines how accurate, computationally efficient real-time plant detection can be achieved on the large and varied benchmarking Open Plant Phenotyping Database, by building upon the State-of-the-Art (SoA) Scaled YOLO v4 real-time object detection model. The effect of pre-processing, namely cropping unnecessary information and increasing contrast, is examined and experimentally shown to improve both accuracy and efficiency. Transfer learning is also leveraged for the deployment of Scaled YOLO v4, using pre-trained weights from the MS COCO data set, and shown to lead to a moderate improvement in accuracy. The proposed final model results in approximately 10% higher accuracy than the existing baseline model, on a representative subset of about half of images in the Open Plant Phenotyping Database. Experiments show that plant detection accuracy is improved for most well represented samples, with errors appearing in particularly challenging cases or caused by data imbalance. This shows the proposed method has significant potential for highly accurate and computationally efficient plant detection in real-world environments.