Utilizing deep learning algorithms for fruit ripening stage classification

• Published in: AIP conference proceedings Vol. 3112; no. 1
• Main Authors: Thambi, Pon Bharathi Asai, Ramalingam, Lakshmi, Suresh, Anjana, Sebastian, Renswick
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Melville American Institute of Physics 03.06.2024
• Subjects: Algorithms; Artificial neural networks; Computer vision; Deep learning; Image classification; Machine learning; Ripening
• ISSN: 0094-243X; 1935-0465; 1551-7616; 1551-7616
• DOI: 10.1063/5.0211339

Image classification plays a vital role in classifying different objects without the human supervision. In manual system of image classification is more time consuming process and it is not suitable for future automated systems. The lack of a completely automated, low-cost system for real-time picture categorization highlights the continued difficulty of the subject. In this work, we offer Multiple Fruit Maturity Stage Classification, a method for automatically categorizing the ripeness of various fruits using computer vision and deep learning techniques. Convolution neural network (CNN) is used to extract the appropriate features for accurate classification. When opposed to its forerunners, Convolution neural network’s (CNN) key benefit is that it can discover crucial elements automatically, without human intervention. The system shows better accuracy for both test and validation.