The week's pick
Reseach Article
A Review of Machine Learning Techniques to Enhance Vegetable Grading and Spoilage Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 58 |
| Year of Publication: 2024 |
| Authors: Zainab Usman, Vaishnavi C. Kamatagi, Tilak Matagunde, Raviprakash, Revathi G.P. |
10.5120/ijca2024924332
|
Zainab Usman, Vaishnavi C. Kamatagi, Tilak Matagunde, Raviprakash, Revathi G.P. . A Review of Machine Learning Techniques to Enhance Vegetable Grading and Spoilage Detection. International Journal of Computer Applications. 186, 58 ( Dec 2024), 1-10. DOI=10.5120/ijca2024924332
Abstract
Examining the nexus of machine learning and farming, this paper navigates through the landscape of AI-driven fruit and vegetable grading, revealing strides in accuracy alongside ongoing computational and quality related hurdles. YOLOv5, Faster R-CNN, and Vision Transformers (ViT) are some of the techniques reviewed for object detection and classification tasks that have very high accuracy with real-time performance. This survey presents a novel approach for fruit and vegetable classification through modified K-Means, SVM, and ANN techniques with remarkable efficiency and cost reducing abilities. The discussion incorporates traditional edge detection methods like Sobel, Prewitt, and Canny, along with innovative approaches like GrabCut and DeepSORT, for contributions in the field of image segmentation and object counting. Despite improvements with high impact, overfitting, dependence on the quality of images, and computational costs remain, which require continued research. This paper surveys the current methodologies in agricultural Machine Learning and Deep Learning applications and presents some directions of future improvements.
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