Automated and Non-destructive YOLOv5 Silique Count Software MuSiC v 1.0 Aided Selection of Drought Tolerant Mustard Genotypes

Object detection is a crucial aspect of computer vision used to address drought tolerance in the biological context. In this context, we address the crucial task of selection of drought-tolerant mustard genotypes (B. juncea) based on automatic and non-destructive deep learning YOLOv5-based silique count software MuSiC v1.0. Our approach utilizes YOLOv5, based on images acquired using a DSLR camera (SONY Alpha 7iii, 24.2MP), securely affixed to a tripod. The user-friendly desktop software named MuSiC v1.0, underpinned by the YOLOv5 model discriminates drought tolerant and susceptible mustard genotypes based on Multi-trait Genotypte Ideotype Distance Index (MGIDI) and the results are comparable with the manual count. This model was trained using an annotated dataset for each of the 30 genotypes, crafted with the assistance of the Roboflow annotator, comprising approximately 22,800 annotations. Drought effect on the silique count trait was comparable both in manual as well as MuSiC v1.0 software-based count method. Stress tolerance indices are derived using iPASTIC software for 30 genotypes and ultimately these traits are used for selecting drought tolerance based on MGIDI index. Superior drought-tolerant mustard genotypes were chosen with a 25% selection intensity, showing around a 70% similarity between manual- and software-based counts.