Bio-Inspired Automated Crop Quality Assessment using Computer Vision

As the demand for global food rising, the need for sustainable farming practices becomes more urgent, and the accurate assessment of crop quality becomes increasingly critical. Traditional methods of crop evaluation are labor-intensive, time-consuming, and susceptible to human error, leading to inconsistent results that adversely affect agricultural productivity. This work develops an automated crop quality assessment system utilizing computer vision and deep learning techniques to enhance the efficiency and accuracy of crop evaluations. The primary purpose of this system is to provide farmers, vendors, and retailers with reliable tools for assessing crop quality, facilitating informed decision-making, and reducing waste. By classifying crops into three categories— Good, Medium, and Bad—the system supports better market efficiency and promotes eco-friendly agricultural practices. This work employs Convolutional Neural Networks (CNNs), specifically leveraging pre-trained models such as VGG16 and EfficientNetB0 for feature extraction and classification. The methodology involves collecting a diverse dataset of crop images under varying environmental conditions, followed by data augmentation to simulate real-world variations and improve model generalization. The system is designed with a user-friendly interface that allows stakeholders to upload images and receive immediate quality assessments. The results demonstrate high accuracy, validated through precision, recall, and confusion matrix analysis. Ultimately, this work transforms traditional crop assessment methods, empowering stakeholders with advanced technological solutions for improved agricultural management.