Object detection is a challenging and important field in the domain of computer vision. Automated detection and counting of the number of siliques non-destructively, which is the most important yield component trait in Indian mustard (B. juncea), which is important as well as widely cultivated oilseed in India, is done here. We have used Deep Learning based one-stage object detection model, YOLOv5 in the counting of mustard siliques from the images captured by a DSLR camera (SONY Alpha 7iii, 24.2MP) manually mounted on a tripod. A GUI-based desktop software, MuSiC v1.0 is developed using Tkinter, Python backed by YOLOv5 model developed from annotated training dataset using Roboflow annotator. Approximately 22,800 annotations were used to train the deep learning YOLOv5 model and the whole dataset was partitioned into training, testing and validation in the ratio of 8:1:1. The Confusion matrix, F1 score and mAP showed a moderate result of 66%, 0.51 and 0.38, respectively, with the handcrafted ground truth data. The deployed model in MuSiC v1.0 showed a promising result of approximately good accuracy for silique count. This is the first initiative of creation of a desktop application for mustard silique count non-destructively.