A Novel Convolutional Neural Network-Based Framework for Complex Multiclass Brassica Seed Classification

TL;DR

This paper introduces a new CNN-based framework for accurately classifying ten Brassica seed types, addressing texture similarity challenges and outperforming existing models with 93% accuracy.

Contribution

The study presents a novel CNN architecture tailored for seed classification, improving accuracy over pre-trained models in distinguishing similar seed textures.

Findings

Achieved 93% classification accuracy on Brassica seed dataset.

Outperformed several pre-trained state-of-the-art architectures.

Addressed texture similarity challenge in seed images.

Abstract

Agricultural research has accelerated in recent years, yet farmers often lack the time and resources for on-farm research due to the demands of crop production and farm operations. Seed classification offers valuable insights into quality control, production efficiency, and impurity detection. Early identification of seed types is critical to reducing the cost and risk associated with field emergence, which can lead to yield losses or disruptions in downstream processes like harvesting. Seed sampling supports growers in monitoring and managing seed quality, improving precision in determining seed purity levels, guiding management adjustments, and enhancing yield estimations. This study proposes a novel convolutional neural network (CNN)-based framework for the efficient classification of ten common Brassica seed types. The approach addresses the inherent challenge of texture similarity in seed images using a custom-designed CNN architecture. The model's performance was evaluated against several pre-trained state-of-the-art architectures, with adjustments to layer configurations for optimized classification. Experimental results using our collected Brassica seed dataset demonstrate that the proposed model achieved a high accuracy rate of 93 percent.