Model for predicting the level of productivity of Cowpea (Vigna Uguiculata) sprout germination using computer vision

Abstract

This dissertation explores the impact of estimating the level of productivity of cowpea (vigna unguiculata) germination using computer vision. The primary objective was to develop a model for monitoring and estimating the level of germination in vigna crops using computer vision in Unyama Municipality in Gulu Uganda. Using experimental, machine learning methods of Convolutional Neural Networks (CNN), and ResNet50 FPN, the study investigated germination. The findings indicate that the development, training, and testing of a deep-learning model for estimating levels of vigna unguiculata (cowpeas). The primary dataset for model training and validation was obtained from a controlled experimental greenhouse environment. An open-source dataset from Kaggle was used to evaluate the accuracy of the object detection model over three epochs. Intersection over union (IoU) match of 100% was reported, indicating a perfect match. A sprout success rate 64% based on leaf count was reported. The study contributes to artificial intelligence in the field of computer vision by Contributed to the field of knowledge, creation of a vigna dataset, development of a vigna detection model, detect and count the number of vigna, identification and classification of objects within the dataset. Keywords: ResNet50 FPN, Convolutional Neural Networks (CNN), Germination.