Work

Precised Cashew Classification Using Machine Learning

Year: 2024

Type: article

Abstract: This study explores the utilization of deep learning techniques for accurate cashew classification to improve efficiency and accuracy in the cashew industry. YOLOv5, YOLOv9, and a Convolutional Neural Network (CNN) were evaluated in classifying cashews into whole, broken, split-up, split-down, and defect categories. A comprehensive labeled dataset was built to train the models, applying data augmentation to increase robustness. YOLOv5 achieved the highest accuracy of 97.65% and the fastest inference time (0.025 s per image) among the models, making it suitable for real-time applications. Although CNN offered a simpler architecture, YOLOv5's superior performance places it as the most promising candidate for large-scale cashew classification deployment.

Source: Engineering Technology & Applied Science Research

DOI: 10.48084/etasr.8052

Authors: Sowmya Nag Karnam, Veenadevi Siddanahundi Vaddagallaiah, Pradeep Kooganahalli Rangnaik, Akshaya Kumar, Charan Kumar, Bidadi Mahesh Vishwanath

Institutions: R.V. College of Engineering, Visvesvaraya Technological University

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $431