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Relevant bibliographies by topics / Fruits Fresh and Rotten dataset

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Academic literature on the topic 'Fruits Fresh and Rotten dataset'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Fruits Fresh and Rotten dataset"

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Hariprasath., S., R. Deepikha., R. Agalya., Ranijth. S. Bauma, and Manickam. R. Gopi. "Design and Implementation of Fruits Classification System using Machine Learning Algorithms." International Journal of Multidisciplinary Research Transactions 5, no. 7 (2023): 88–99. https://doi.org/10.5281/zenodo.7922084.

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Detecting the rotten fruits become significant in the agricultural industry. Usually, the classification of fresh and rotten fruits is carried by humans is not effectual for the fruit farmers. Human beings will become tired after doing the same task multiple times, but machines do not. Thus, the project proposes an approach to reduce human efforts, reduce the cost and time for production by identifying the defects in the fruits in the agricultural industry. If we do not detect those defects, those defected fruits may contaminate good fruits. Hence, we proposed a model to avoid the spread of ro

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Dnyaneshwar V Dhande and Dinesh D Patil. "A Deep Learning based Model for Fruit Grading using DenseNet." International Journal of Engineering and Management Research 12, no. 5 (2022): 6–10. http://dx.doi.org/10.31033/ijemr.12.5.2.

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Detecting the rotten fruits become significant in the agricultural industry. Usually, the classification of fresh and rotten fruits is carried by humans is not effectual for the fruit farmers. Human beings will become tired after doing the same task multiple times, but machines do not. Thus, this paper proposes an approach to reduce human efforts, reduce the cost and time for production by identifying the defects in the fruits in the agricultural industry. If we do not detect those defects, those defected fruits may contaminate good fruits. Hence, we proposed a model to avoid the spread of rot

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Srinivas, R. "Deep Learning based Fruit Quality Inspection." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 4535–39. http://dx.doi.org/10.22214/ijraset.2022.44928.

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Abstract: Digital images and computer sciences have become two powerful tools in several areas, such as astronomy, medicine, forensics, etc. In the last few years, computer sciences are getting involved in agricultural and food science to decide based on estimated or actual parameters named features. Rottenness is the state of decomposing or decaying the quality of the fruit, which not only affects the taste and appearance but also modifies its nutritional composition, causing the presence of mycotoxins dangerous for humans. Detecting rotten fruits has become significant in the agricultural in

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Palakodati, Sai Sudha Sonali, Venkata RamiReddy Chirra, Yakobu Dasari, and Suneetha Bulla. "Fresh and Rotten Fruits Classification Using CNN and Transfer Learning." Revue d'Intelligence Artificielle 34, no. 5 (2020): 617–22. http://dx.doi.org/10.18280/ria.340512.

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Detecting the rotten fruits become significant in the agricultural industry. Usually, the classification of fresh and rotten fruits is carried by humans is not effectual for the fruit farmers. Human beings will become tired after doing the same task multiple times, but machines do not. Thus, the project proposes an approach to reduce human efforts, reduce the cost and time for production by identifying the defects in the fruits in the agricultural industry. If we do not detect those defects, those defected fruits may contaminate good fruits. Hence, we proposed a model to avoid the spread of ro

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D, Ms Suseela, Varsha S, Bharaneedharan C, and Lekshana Shivani C. "CLASSIFICATION OF FRESH AND ROTTEN FRUITS USING DIFFERENT CNN MODELS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem26057.

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Fruit freshness automated classification is crucial to the agricultural sector. In the traditional procedure, a human being grades the fruit. Additionally, this process is labor-intensive, time-consuming, and ineffective. Additionally, it raises production costs. Therefore, a quick, precise, and automated system that may lessen human effort, enhance production, and decrease manufacturing time and cost is needed for industrial applications. The deep learning- based model for classifying fruit freshness is used in the current work. Various Convolution Neural Network (CNN) models are proposed, an

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Saputra, Andika Jodhi, and Widyastuti Andriyani. "Fruit Image Classification Using Naive Bayes Algorithm with Histogram of Oriented Gradients (HOG) Feature Extraction." Journal of Artificial Intelligence and Software Engineering (J-AISE) 5, no. 1 (2025): 215. https://doi.org/10.30811/jaise.v5i1.6536.

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A classification system using Naïve Bayes algorithm was developed to distinguish between fresh and rotten fruits, specifically apples, bananas and oranges. This research utilized a dataset consisting of 13,599 images and applied the Histogram of Oriented Gradients (HOG) technique for feature extraction, followed by model training and evaluation. The results showed that the Naïve Bayes algorithm achieved an accuracy of 87%, with the highest precision in the fresh apple class (0.9792) and the highest recall in the rotten apple class (0.9843). The rotten banana class showed a balanced performance

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Napitu, Stifani, Rini Paramita Panjaitan, Putri Aisyah Nulhakim, and Muaz Khalik Lubis. "Klasifikasi Buah Jeruk Segar dan Busuk Berdasarkan RGB dan HSV Menggunakan Metode KNN." Jurnal SAINTEKOM 13, no. 2 (2023): 214–21. http://dx.doi.org/10.33020/saintekom.v13i2.420.

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Fruits are a group of agricultural commodities in Indonesia. The demand for domestic fruit commodities is quite high, this is indicated by the large number of fruits available in modern markets and traditional markets. In this research, a classification process will be carried out between fresh oranges and rotten oranges based on RGB (Red, Green, Blue) and HSV (Hue, Saturation, Value) color extraction. This study uses the K-Nearest Neighbor classification algorithm with a value of k = 1; 2; 3; 4; 5; 6; and 7. The dataset used consists of 146 training data and 88 testing data. The purpose and b

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Carlos Arias, Camilo Baldovino, José Gómez, Brian Restrepo, and Sergio Sánchez. "Deep learning model for recognizing fresh and rotten fruits in industrial processes." Transactions on Energy Systems and Engineering Applications 6, no. 1 (2025): 1–14. https://doi.org/10.32397/tesea.vol6.n1.811.

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The detection of fruit condition is essential to ensure quality control in industrial processes. Currently, this task is often performed manually, which is inefficient and time-consuming for operators. Therefore, it is crucial to implement emerging technologies that reduce human effort, costs, and production time while enabling more effective defect detection in fruits. In this context, this work presents the implementation of an artificial intelligence model based on computer vision to identify the condition of fruits. Various models were compared, including YOLOv8, YOLOv11, Detectron2, and F

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Ashraf, Shawon, Ivan Kadery, Md Abdul Ahad Chowdhury, Tahsin Zahin Mahbub, and Rashedur M. Rahman. "Fruit Image Classification Using Convolutional Neural Networks." International Journal of Software Innovation 7, no. 4 (2019): 51–70. http://dx.doi.org/10.4018/ijsi.2019100103.

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Convolutional neural networks (CNN) are the most popular class of models for image recognition and classification task nowadays. Most of the superstores and fruit vendors resort to human inspection to check the quality of the fruits stored in their inventory. However, this process can be automated. We propose a system that can be trained with a fruit image dataset and then detect whether a fruit is rotten or fresh from an input image. We built the initial model using the Inception V3 model and trained with our dataset applying transfer learning.

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Aksoy, Serra, Pinar Demircioglu, and Ismail Bogrekci. "Evaluating pre-trained CNNs for distinguishing fresh vs rotten fruits and vegetables." Journal of Applied Horticulture 26, no. 3 (2024): 361–66. https://doi.org/10.37855/jah.2024.v26i03.68.

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Accurately distinguishing between fresh and rotten fruits and vegetables is essential for reducing waste, ensuring food safety, and maintaining quality standards in agriculture and supply chain management. This research utilized the fruit and vegetable diseases dataset from Kaggle, which included images of 14 types of produce in both healthy and rotten states. In this study, the performance of four pre-trained convolutional neural network models was evaluated: MobileNetV3 Small, EfficientNetV2 Small, DenseNet121, and ShuffleNetV2_x1_5. Among these, ShuffleNetV2_x1_5 demonstrated the highest pe

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Book chapters on the topic "Fruits Fresh and Rotten dataset"

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Chougule, Archana, Apurva Pawar, Rahul Kamble, Juned Mujawar, and Akshay Bhide. "Recognizing Fresh and Rotten Fruits Using Deep Learning Techniques." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0171-2_20.

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Anitha, G., and P. Thiruvannamalai Sivasankar. "Fruits fresh and rotten detection using CNN and transfer learning." In Recent Trends in Computational Sciences. CRC Press, 2023. http://dx.doi.org/10.1201/9781003363781-1.

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Lankton, Larry. "A Lapful of Apples." In Beyond The Boundaries. Oxford University PressNew York, NY, 1997. http://dx.doi.org/10.1093/oso/9780195108040.003.0004.

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Abstract Horace Greeley, remembering the 1840s, recalled that Lake Superior was not “calculated to attract a ... gourmand.” Food was frequently scarce, sometimes rotten, and often monotonous and boring. Pioneers could not eat a year-round diet of nourishing, varied, and appetizing food. But two or three decades of occupation brought a great deal of change to the Keweenaw diet. In the mid- 184os, a hungry man with a shotgun might walk into the forest, ready to target as food virtually anything that moved; by 1870, he could stroll into a church social and sit down to a dish of strawberries and i

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Conference papers on the topic "Fruits Fresh and Rotten dataset"

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Sharma, Jatin, and Bura Vijay Kumar. "Automated Classification of Fresh and Rotten Fruits Using ResNet50 for Enhanced Food Quality Control and Waste Reduction1." In 2025 International Conference on Pervasive Computational Technologies (ICPCT). IEEE, 2025. https://doi.org/10.1109/icpct64145.2025.10939134.

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Mishra, Prashant Kumar, and Jagrati Singh. "Rotten and Fresh Fruits Classification using Deep Learning." In 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT). IEEE, 2024. http://dx.doi.org/10.1109/ic2pct60090.2024.10486807.

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Chouhan, Manorama, P. S. Banerjee, Amit Kumar, and Jatin Kushwaha. "Classification of Rotten Fruits vs Fresh Fruits Using Sequential Model with Convolutional Neural Network." In 2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). IEEE, 2023. http://dx.doi.org/10.1109/icccis60361.2023.10425430.

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Nerella, J. N. V. D. Tanuia, Vamsi Krishna Nippulapalli, Srivani Nancharla, Lakshmi Priya Vellanki, and Pallikonda Sarah Suhasini. "Performance Comparison of Deep Learning Techniques for Classification of Fruits as Fresh and Rotten." In 2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI). IEEE, 2023. http://dx.doi.org/10.1109/raeeucci57140.2023.10134242.

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Miah, Md Sohel, Tayeeba Tasnuva, Mirajul Islam, Mumenunnessa Keya, Md Riazur Rahman, and Syed Akhter Hossain. "An Advanced Method of Identification Fresh and Rotten Fruits using Different Convolutional Neural Networks." In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2021. http://dx.doi.org/10.1109/icccnt51525.2021.9580117.

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