Detection and quantification of cotton trichomes by deep learning algorithm

Cotton is one of the most economically important agricultural crops with the harvest of cotton fibers. Trichomes are initiated from the plant epidermis, which is responsible for plant resistance to many abiotic and biotic stresses. Cotton fibers share similar underlying developmental molecular mechanisms with trichomes on the stem and leaf margins of cotton. Compared with fibers, it is easier to observe the trichome phenotype of cotton stem and leaf margins. However, because of the variability in coloration and the high occlusion of the trichomes, counting the density and observing the distribution of trichomes is still highly laborious. To help resolve this problem, the present research provides a solution based on deep learning in the screening of cotton trichomes, which makes it possible to monitor the phenotype of the cotton efficiently. With a fluorescent microscopy im-aging procedure, we clearly observed that there are two types of trichome phenotype in Gossypium hirsutum ('TM-1 '), which are singled and clustered trichomes. The clustered trichome originates from two or more adjacent epidermal cells with no branches. The TM1 trichome was used to create a collection of singled and clustered trichome pictures. Following data production, we evaluated the effectiveness of several you only look once (YOLO) model iterations in the context of identifying singled and clustered trichomes, respectively. Results show that the YOLOv5s model outperforms YOLOv3 and YOLOv4 in average accuracy, recall, accuracy, and F1 score. We utilized the clustered trichome data set to assess the performance of YOLOv5s and Mask R-CNN in order to more precisely detect the real trichome distribution with simultaneous detection of single and clustered tri-chomes. The obtained findings show that both models may be utilized to concurrently identify singled and clustered trichomes since the discrepancy between the detected values of the two models and the real values is quite minimal. Mask R-CNN has a high IOU value, whereas YOLOv5s has a low false-positive detection rate and fast detection speed. Our models and datasets can be used as a starting point to measure the trichome trait in other cotton species and establish a cotton trichome detection and counting system based on deep learning technology.