Database/Software Article | Open Access
Volume 2024 |Article ID 0234 | https://doi.org/10.34133/plantphenomics.0234

Deep Learning Methods Using Imagery from a Smartphone for Recognizing Sorghum Panicles and Counting Grains at a Plant Level

Gustavo N. Santiago ,1 Pedro H. Cisdeli Magalhaes,1 Ana J. P. Carcedo,1 Lucia Marziotte,1 Laura Mayor,2 Ignacio A. Ciampitti 1,3

1Department of Agronomy, Kansas State University, Manhattan, KS 66506, USA
2Corteva Agriscience,Wamego, KS 66547, USA
3Institute of Digital Agriculture and Advanced Analytics, Kansas State University,Manhattan, KS 66506, USA

Received 
29 Dec 2023
Accepted 
22 Jul 2024
Published
28 Aug 2024

Abstract

High-throughput phenotyping is the bottleneck for advancing field trait characterization and yield improvement in major field crops. Specifically for sorghum (Sorghum bicolor L.), rapid plant-level yield estimation is highly dependent on characterizing the number of grains within a panicle. In this context, the integration of computer vision and artificial intelligence algorithms with traditional field phenotyping can be a critical solution to reduce labor costs and time. Therefore, this study aims to improve sorghum panicle detection and grain number estimation from smartphone-capture images under field conditions. A preharvest benchmark dataset was collected at field scale (2023 season, Kansas, USA), with 648 images of sorghum panicles retrieved via smartphone device, and grain number counted. Each sorghum panicle image was manually labeled, and the images were augmented. Two models were trained using the Detectron2 and Yolov8 frameworks for detection and segmentation, with an average precision of 75% and 89%, respectively. For the grain number, 3 models were trained: MCNN (multiscale convolutional neural network), TCNN-Seed (two-column CNN-Seed), and Sorghum-Net (developed in this study). The Sorghum-Net model showed a mean absolute percentage error of 17%, surpassing the other models. Lastly, a simple equation was presented to relate the count from the model (using images from only one side of the panicle) to the field-derived observed number of grains per sorghum panicle. The resulting framework obtained an estimation of grain number with a 17% error. The proposed framework lays the foundation for the development of a more robust application to estimate sorghum yield using images from a smartphone at the plant level.

© 2019-2023   Plant Phenomics. All rights Reserved.  ISSN 2643-6515.

Back to top