Seed Germination Prediction using Machine Learning Approach

Agriculture is an important part of Pakistan’s economy, and maize is one of the country’s major crops. Seed germination plays a key role in crop productivity, but traditional germination testing methods are slow, labor-intensive, and require 7–14 days for results. To overcome these limitations, this Final Year Project presents an AI-based system for automated maize seed germination prediction using RGB images. The proposed system uses Machine Learning, Deep Learning, and Computer Vision techniques to classify maize seeds as germinated or non-germinated. A custom dataset of maize seed images was collected under both laboratory and real field conditions. Image preprocessing techniques such as resizing and normalization were applied to improve model performance. Two deep learning models, Custom CNN and MobileNetV2, were developed and compared for accurate seed classification. The system achieved high accuracy and provided fast, reliable, and non-destructive prediction results. Unlike traditional methods, the proposed approach predicts seed viability within seconds using only seed images. This AI-powered solution supports sustainable and precision agriculture by reducing seed waste, optimizing resources, and improving maize productivity for farmers. The system can also be integrated into a mobile application for real-time, on-field decision-making, making it a practical and efficient tool for smart agriculture.

Poster