Classification of Healthy Seeds Using Deep Learning

Abstract

With the increasing demand for healthy and high-quality seeds in agriculture, accurate and efficient seed classification methods are essential for seed quality control and optimisation of crop production. This work utilises a deep learning-based approach for healthy seed classification. It proposes a deep learning-based approach for beneficial seed classification, leveraging the power of neural networks to learn discriminative features from seed images automatically.
The proposed method involves a multi-step pipeline that includes Image preprocessing, and Classification. The seed images are initially preprocessed to enhance their quality and reduce noise using image normalisation and denoising techniques. Next, a Deep convolutional neural network (CNN) is employed to extract relevant features from the preprocessed seed images. The CNN model is designed to capture the seeds' local and global characteristics, enabling it to learn complex patterns and textures.