Leaf Counting in Rice (Oryza Sativa L.) Using Object Detection: A Deep Learning Approach

Leaf count is one of the crucial tasks in plant phenotyping, and leaves are the basic unit of plant architecture involved in photosynthesis, growth, and yield of a plant. Therefore, the total number of leaves per plant is considered as one of the essential physio-morphological plant traits for phenotyping. The current work proposes to estimate the total number of leaves of a rice plant by detecting their leaves tips. A rice plant has a single tip for a single leaf. Hence, this proposed framework counts the total number of leaves by counting the number of leaves tips equal to the number of leaves. You Only Look Once (YOLO) algorithm is used for the detection of the leaves tips as an object. This hypothesis builds a basis for counting the total number of leaves in a plant like rice, and similar field crops such as wheat (Triticum aestivum L), maize (Zea mays L.), sorghum (Sorghum bicolor), barley (Hordeum vulgare L.). The model detected leaves of a rice plant (RGB images) by detecting corresponding leaves tips with YOLO having average accuracy up to 82% and IOU around 0.53-0.60 and estimates the number of leaves in a plant by counting predicted bounding boxes around tips. The model also performed well with the wheat crop.