Log in

Relevant bibliographies by topics / Fruits Fresh and Rotten dataset / Journal articles

To see the other types of publications on this topic, follow the link: Fruits Fresh and Rotten dataset.

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

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Fruits Fresh and Rotten dataset.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

5

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

11

Ananthanarayana, Tejaswini, Raymond Ptucha, and Sean C. Kelly. "Deep Learning based Fruit Freshness Classification and Detection with CMOS Image sensors and Edge processors." Electronic Imaging 2020, no. 12 (2020): 172–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.12.fais-172.

Full text

Abstract:

CMOS Image sensors play a vital role in the exponentially growing field of Artificial Intelligence (AI). Applications like image classification, object detection and tracking are just some of the many problems now solved with the help of AI, and specifically deep learning. In this work, we target image classification to discern between six categories of fruits — fresh/ rotten apples, fresh/ rotten oranges, fresh/ rotten bananas. Using images captured from high speed CMOS sensors along with lightweight CNN architectures, we show the results on various edge platforms. Specifically, we show resul

APA, Harvard, Vancouver, ISO, and other styles

12

Kumar, T. Bharath, Deepak Prashar, Gayatri Vaidya, Vipin Kumar, S. Deva Kumar, and F. Sammy. "A Novel Model to Detect and Classify Fresh and Damaged Fruits to Reduce Food Waste Using a Deep Learning Technique." Journal of Food Quality 2022 (May 23, 2022): 1–8. http://dx.doi.org/10.1155/2022/4661108.

Full text

Abstract:

Due to a lack of efficient measures for dealing with food waste at many levels, including food supply chains, homes, and restaurants, the world’s food supply is shrinking at an alarming pace. In both homes and restaurants, overcooking and other factors are to be blamed for the majority of food that is wasted. Families are the primary source of food waste, and we sought to reduce this by identifying fresh and damaged food. In agriculture, the detection of rotting fruits becomes crucial. Despite the fact that people routinely classify healthy and rotten fruits, fruit growers find it ineffective.

APA, Harvard, Vancouver, ISO, and other styles

13

Mukhiddinov, Mukhriddin, Azamjon Muminov, and Jinsoo Cho. "Improved Classification Approach for Fruits and Vegetables Freshness Based on Deep Learning." Sensors 22, no. 21 (2022): 8192. http://dx.doi.org/10.3390/s22218192.

Full text

Abstract:

Classification of fruit and vegetable freshness plays an essential role in the food industry. Freshness is a fundamental measure of fruit and vegetable quality that directly affects the physical health and purchasing motivation of consumers. In addition, it is a significant determinant of market price; thus, it is imperative to study the freshness of fruits and vegetables. Owing to similarities in color, texture, and external environmental changes, such as shadows, lighting, and complex backgrounds, the automatic recognition and classification of fruits and vegetables using machine vision is c

APA, Harvard, Vancouver, ISO, and other styles

14

Baloch, Abdul Khalique, Prof Dr Ali Okatan, Mujeeb-ur-Rehman Jamali, Nadeem Ahmed Kanasro, Muhammad Ali Baloch, and Asad Ali Jamali. "The Quality Analysis of Food and Vegetable from Image Processing." VAWKUM Transactions on Computer Sciences 11, no. 2 (2023): 01–17. http://dx.doi.org/10.21015/vtcs.v11i2.1582.

Full text

Abstract:

The quality analysis of food and vegetable from image is hot topic now a day, where researchers make them better then pervious findings through different technique and methods. In this research we have review the literature, and find gape from them, and suggest better proposed approach, design the algorithm, developed a software to measure the quality from images, where accuracy of image show better results, and compare the results with Perouse work done so for. The Application we use an open-source dataset and python language with tensor flow lite framework. In this research we focus to sort

APA, Harvard, Vancouver, ISO, and other styles

15

B D, Varalakshmi, Vijayalakshmi S A, M. Kavya Sree, Ayushi Raj, Deepika Divya, and Anubhav Tekriwal. "AI-Enabled Fruit Decay Detection." Computer Science & Engineering: An International Journal 15, no. 1 (2025): 159–66. https://doi.org/10.5121/cseij.2025.15118.

Full text

Abstract:

In recent years, the significance of fruit classification has witnessed a remarkable surge across various industries, owing to its pivotal role in delineating fruit species, shaping pricing strategies, and ensuring overall quality, especially concerning the exportation of fresh produce. This study capitalizes on a substantial dataset comprising 5658 meticulously curated fruit images categorized into 10 distinct classes, harnessing the power of convolutional neural network (CNN) models for precise classification endeavors. The deployment of automated classification systems emerges as indispensa

APA, Harvard, Vancouver, ISO, and other styles

16

Wibowo, Ari, Lusiana Lusiana, and Tika Kartika Dewi. "Implementasi Algoritma Deep Learning You Only Look Once (YOLOv5) Untuk Deteksi Buah Segar Dan Busuk." Paspalum: Jurnal Ilmiah Pertanian 11, no. 1 (2023): 123. http://dx.doi.org/10.35138/paspalum.v11i1.489.

Full text

Abstract:

Fruit is one of the nutritional needs for the body that must be met. But with a note, these nutrients will be obtained from fruit that is still fresh. The definition of fresh fruit itself is fruit that can be consumed directly and does not require any further processing. There are many ways to select and differentiate between fresh fruit and bad fruit and in general direct observations can be made. But over time, there are several other ways to observe fruit freshness using existing technology. Where one of them is by optimizing deep learning and machine learning. This detection and classifica

APA, Harvard, Vancouver, ISO, and other styles

17

Roman, Ali, Md Mostafizer Rahman, Sajjad Ali Haider, Tallha Akram, and Syed Rameez Naqvi. "Integrating Feature Selection and Deep Learning: A Hybrid Approach for Smart Agriculture Applications." Algorithms 18, no. 4 (2025): 222. https://doi.org/10.3390/a18040222.

Full text

Abstract:

This research tackles the critical challenge of achieving precise and efficient feature selection in machine learning-based classification, particularly for smart agriculture, where existing methods often fail to balance exploration and exploitation in complex, high-dimensional datasets. While current approaches, such as standalone nature-inspired optimization algorithms, leverage biological behaviors for feature selection, they are limited by their inability to synergize diverse strategies, resulting in suboptimal performance and scalability. To address this, we introduce the Hybrid Predator

APA, Harvard, Vancouver, ISO, and other styles

18

Munfaati, Eka Aenun Nisa, and Arita Witanti. "Klasifikasi Buah dan Sayuran Segar atau Busuk Menggunakan Convolutional Neural Network." JISKA (Jurnal Informatika Sunan Kalijaga) 9, no. 1 (2024): 27–38. http://dx.doi.org/10.14421/jiska.2024.9.1.27-38.

Full text

Abstract:

Fresh fruits and vegetables contain many nutrients, such as minerals, vitamins, antioxidants, and beneficial fiber, superior to those found in rotten or almost rotten produce. On the other hand, fruits and vegetables that are nearly spoiled or already rotten have significantly lost their nutritional value. Rotten produce also harbors bacteria and fungi that can lead to infections and food poisoning when consumed. Convolutional Neural Network (CNN) offers a programmable solution for classifying fresh and rotten fruits and vegetables. Image processing using the TensorFlow library is employed in

APA, Harvard, Vancouver, ISO, and other styles

19

Ertürk, Aliye, and Ömer Ertürk. "The Antioxidant and Antimicrobial Activities of Some Rotten and Fresh Fruits, Vegetables Extracts." Ordu Üniversitesi Bilim ve Teknoloji Dergisi 14, no. 1 (2024): 9–23. http://dx.doi.org/10.54370/ordubtd.1272380.

Full text

Abstract:

This study evaluated the antimicrobial and antioxidant activities of some fresh fruits and vegetables and their rotten forms. Among the fresh and rotten materials examined, there were Citrus paradise, Citrus sinensis, Punica granatum, Cydonia oblonga, Malus domestica, Citrus limon, Pyrus anatolica, Persea americana, Capsicum annuum var., Actinidia deliciosa, Beta vulgaris L. It was already known that fresh fruits, vegetables have potential microbicidal activities. But how the rottens would behave is unknown. Antimicrobial activities of fresh and rotten samples were examined on selected bacteri

APA, Harvard, Vancouver, ISO, and other styles

20

Azim, Sayed Mehedi, Austin Spadaro, Joseph Kawash, James Polashock, and Iman Dehzangi. "Accurately Identifying Sound vs. Rotten Cranberries Using Convolutional Neural Network." Information 15, no. 11 (2024): 731. http://dx.doi.org/10.3390/info15110731.

Full text

Abstract:

Cranberries, native to North America, are known for their nutritional value and human health benefits. One hurdle to commercial production is losses due to fruit rot. Cranberry fruit rot results from a complex of more than ten filamentous fungi, challenging breeding for resistance. Nonetheless, our collaborative breeding program has fruit rot resistance as a significant target. This program currently relies heavily on manual sorting of sound vs. rotten cranberries. This process is labor-intensive and time-consuming, prompting the need for an automated classification (sound vs. rotten) system.

APA, Harvard, Vancouver, ISO, and other styles

21

J, SRI HARI HARAN KA. "FRUIT FRESHESS PREDICTION USING CNN." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33633.

Full text

Abstract:

This Project proposes a methodology utilizing Convolutional Neural Networks (CNNs) for the holistic assessment of fruit quality, encompassing freshness, ripeness, and health status. By integrating CNNs with multi-feature analysis, including image data and spectral signatures, the model aims to provide accurate classification of fruits into categories of fresh/rotten, ripe/unripe, and healthy/diseased. The methodology outlines data collection, preprocessing, feature extraction, CNN model development, evaluation metrics, and potential applications. Ensuring fruit quality is crucial for consumer

APA, Harvard, Vancouver, ISO, and other styles

22

Sumathi, K., and Viji Vinod. "Classification of fruits ripeness using CNN with multivariate analysis by SGD." Neural Network World 32, no. 6 (2022): 319–32. http://dx.doi.org/10.14311/nnw.2022.32.019.

Full text

Abstract:

Ripeness estimation of fruits is an essential process that impact the quality of fruits and its marketing. Nearly 30% to 35% get wasted from the harvested fruits due to lack of skilled workers in classification and fruit grading. Although it can be executed by human assessment, it is time consuming, costlier and error prone. Lot of research is carried to automate the quality assessment of fruits. Several hyper-parameters have been considered which have liven up by providing robust convolutional neural network (CNN). This paper has focused on image resizer stochastic gradient descent (SGD) algo

APA, Harvard, Vancouver, ISO, and other styles

23

Nosseir, Ann, and Seif Eldin A. Ahmed. "Automatic Classification for Fruits’ Types and Identification of Rotten Ones Using k-NN and SVM." International Journal of Online and Biomedical Engineering (iJOE) 15, no. 03 (2019): 47. http://dx.doi.org/10.3991/ijoe.v15i03.9832.

Full text

Abstract:

Having a system that classifies different types of fruits and identifies the quality of fruits will be of a value in various areas especially in an area of mass production of fruits’ products. This paper presents a novel system that differentiates between four fruits types and identifies the decayed ones from the fresh. The algorithms used are based on the colour and the texture features of the fruits’ images. The algorithms extract the RGB values and the first statistical order and second statistical of the Gray Level Co-occurrence Matrix (GLCM) values. To segregate between the fruits’ types,

APA, Harvard, Vancouver, ISO, and other styles

24

Wasankar, Prof Snehal. "Rotten Fruit Detection Using IoT." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 3422–28. http://dx.doi.org/10.22214/ijraset.2024.59667.

Full text

Abstract:

Abstract: In order to tackle the ongoing problem of keeping fruits fresh, this research presents an automated freshness detection device. The main goal is to create a device that can test the gaseous content of fruit to assess its quality. The Arduino cloud is used in a way of making and controlling Arduino projects online. You can connect your Arduino boards to the internet and send or receive data from them. You can also create web pages or apps to show and control your projects. Arduino cloud computing is easy to use and does not require much coding. Node MCU esp8266 which we use to store a

APA, Harvard, Vancouver, ISO, and other styles

25

Cherniazova, E. A., A. A. Efremova, and N. I. Naumova. "QUALITY AND SAFETY OF FRESH FRUITS." Innovations and Food Safety, no. 1 (March 28, 2019): 36–41. http://dx.doi.org/10.31677/2311-0651-2019-23-1-36-41.

Full text

Abstract:

Plum fruits have harmonious taste qualities and valuable biochemical composition. Plum is characterized by a low content of vitamin C, but in combination with high concentrations of phenolic compounds, is of great value as a source of antioxidants. The problem of improving the quality and ensuring the safety of food products is not only relevant, but is also one of the most important economic problems at the present stage. The aim of the research was to study the quality and safety of fresh plums sold in retail trade. It has been established that in the stores of the retail trade network «Dixi

APA, Harvard, Vancouver, ISO, and other styles

26

Zhang, Tao, Mustafa Mhamed, Qu Zhang, et al. "Apple varieties, diseases, and distinguishing between fresh and rotten through deep learning approaches." PLOS One 20, no. 5 (2025): e0322586. https://doi.org/10.1371/journal.pone.0322586.

Full text

Abstract:

Apples are one of the most productive fruits in the world, in addition to their nutritional and health advantages for humans. Even with the continuous development of AI in agriculture in general and apples in particular, automated systems continue to encounter challenges identifying rotten fruit and variations within the same apple category, as well as similarity in type, color, and shape of different fruit varieties. These issues, in addition to apple diseases, substantially impact the economy, productivity, and marketing quality. In this paper, we first provide a novel comprehensive collecti

APA, Harvard, Vancouver, ISO, and other styles

27

Harsika, Nivasini, J. Janet Priscilla A, Logeswari M, Damini N, and Venilla C. "Thermal Imaging for Egg Freshness Classification using Convolutional Neural Networks with SVM-Based Accuracy Prediction." International Journal of Multidisciplinary Research Transactions 6, no. 5 (2024): 67–78. https://doi.org/10.5281/zenodo.11173878.

Full text

Abstract:

Ensuring the freshness of eggs is critical for both consumer satisfaction and food safety. In this project, we propose a novel approach for egg freshness classification using Convolutional Neural Networks (CNNs) coupled with Support Vector Machine (SVM)-based accuracy prediction. The proposed system aims to accurately classify eggs as fresh or rotten based on images captured through a thermal imaging system. First, we preprocess the egg images to enhance features relevant to freshness assessment. Subsequently, a CNN architecture is trained on a dataset comprising images of both fresh and rotte

APA, Harvard, Vancouver, ISO, and other styles

28

Valentino, Febrian, Tjeng Wawan Cenggoro, Gregorius Natanael Elwirehardja, and Bens Pardamean. "Energy-efficient deep learning model for fruit freshness detection." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1386. http://dx.doi.org/10.11591/ijai.v12.i3.pp1386-1395.

Full text

Abstract:

Fruits are usually used as complementary foods because they contain good nutrients such as protein and vitamins. In addition to having good content, it turns out that there are potentially harmful microorganisms contained in fruits caused by decay. Currently, many artificial intelligence (AI) techniques have been proposed in research related to fruit freshness. Deep learning is one of its most prominent types in similar studies. As deep learning typically requires a lot of computation power, it usually consumes a lot of electricity. This is an important concern, especially for agribusiness com

APA, Harvard, Vancouver, ISO, and other styles

29

Febrian, Valentino, Wawan Cenggoro Tjeng, Natanael Elwirehardja Gregorius, and Pardamean Bens. "Energy-efficient deep learning model for fruit freshness detection." International Journal of Artificial Intelligence (IJ-AI) 12, no. 3 (2023): 1386–95. https://doi.org/10.11591/ijai.v12.i3.pp1386-1395.

Full text

Abstract:

Fruits are usually used as complementary foods because they contain good nutrients such as protein and vitamins. In addition to having good content, it turns out that there are potentially harmful microorganisms contained in fruits caused by decay. Currently, many artificial intelligence (AI) techniques have been proposed in research related to fruit freshness. Deep learning is one of its most prominent types in similar studies. As deep learning typically requires a lot of computation power, it usually consumes a lot of electricity. This is an important concern, especially for agribusiness com

APA, Harvard, Vancouver, ISO, and other styles

30

Poudwal, Leenakshi, Prof Nilesh Rathod, Prathamesh Kagane, and Niraj Shirkar. "Fruit and Quality Detection System to Assist People with Down Syndrome." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 392–97. http://dx.doi.org/10.22214/ijraset.2022.42204.

Full text

Abstract:

Abstract: Fruit classification using a Deep Convolutional Neural Network (CNN) is a promising application of computer Vision in this AI-driven decade. Fruit Recognition has been gaining attraction in recent years due to its application in agriculture and food industry. In this project, we propose a multi-fruit classification scheme based on Deep Convolutional Neural Network and we aim to build a Deep Neural Network to correctly identify the fruits and classify them according to their quality that is- into fresh and rotten. A fruit recognition process usually comprises of three steps: 1. Image

APA, Harvard, Vancouver, ISO, and other styles

31

Abdulwahab, Ismail Durojaiye, Lawan Garba Abubakar, Aminu Mohammed, A. H. Abdulrahman, Ndidiamaka Gladys Nwachukwu, and Danladi D. Usman. "Influence of Acetylene on Ripening Process of Avocado Pear and Orange Fruits." European Journal of Engineering Research and Science 3, no. 12 (2018): 44–49. http://dx.doi.org/10.24018/ejers.2018.3.12.992.

Full text

Abstract:

Postharvest handling on ripening process of avocado and orange fruits was conducted in this study. Ripening systems which include application of acetylene gas generated from overripe banana (T1), storage in airtight drum (T2) and use of smoke (T3) were adopted while fresh samples without any treatment were used as control (C) measure. The results obtained revealed a general trend indicating that both treated climacteric fruits begun to ripen on the third day with scoring values of 3.33±0.33 and 2.67±0.33 for avocado and orange fruits respectively except for control which showed ripening sympto

APA, Harvard, Vancouver, ISO, and other styles

32

Abdulwahab, Ismail Durojaiye, Lawan Garba Abubakar, Aminu Mohammed, A. H. Abdulrahman, Ndidiamaka Gladys Nwachukwu, and Danladi D. Usman. "Influence of Acetylene on Ripening Process of Avocado Pear and Orange Fruits." European Journal of Engineering and Technology Research 3, no. 12 (2018): 44–49. http://dx.doi.org/10.24018/ejeng.2018.3.12.992.

Full text

Abstract:

Postharvest handling on ripening process of avocado and orange fruits was conducted in this study. Ripening systems which include application of acetylene gas generated from overripe banana (T1), storage in airtight drum (T2) and use of smoke (T3) were adopted while fresh samples without any treatment were used as control (C) measure. The results obtained revealed a general trend indicating that both treated climacteric fruits begun to ripen on the third day with scoring values of 3.33±0.33 and 2.67±0.33 for avocado and orange fruits respectively except for control which showed ripening sympto

APA, Harvard, Vancouver, ISO, and other styles

33

Lestari, Febbi Sena, H. Harliana, and Fatra Nonggala Putra. "Automated Detection of Black Pod Disease in Cocoa Fruits Using Convolutional Neural Network." Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) 6, no. 1 (2025): 38–47. https://doi.org/10.30645/kesatria.v6i1.543.

Full text

Abstract:

Black pod disease is a severe disease affecting cocoa fruit, caused by the Phytophthora Palmivora fungus. This infection turns the fruit's surface dark brown to black, while the inside becomes rotten. Currently, identifying infected cocoa fruits is done manually through visual observation, which is prone to errors and inconsistency. This study aims to implement a Convolutional Neural Network (CNN) algorithm to classify images of black pod disease in cocoa fruits. The dataset consists of 1,500 images obtained through documentation and literature review, with 750 images of healthy cocoa fruits a

APA, Harvard, Vancouver, ISO, and other styles

34

Irhebhude, Martins E. "Classification Of Plants By Their Fruits And Leaves Using Convolutional Neural Networks." Science in Information Technology Letters 5, no. 1 (2024): 1–15. http://dx.doi.org/10.31763/sitech.v5i1.1364.

Full text

Abstract:

The population growth of the world is exponential, this makes it imperative that we have an increase in food production. In this light, farmers, industries and researchers are struggling with identifying and classifying food plants. Over the years, there have been challenges that come with identifying fruits manually. It is time-consuming, labour intensive and requires experts to identify fruits because of the similarity in fruit’s leaves (citrus family), shapes, sizes and colour. A computerized detection technique is needed for the classification of fruits. Existing solutions to fruits classi

APA, Harvard, Vancouver, ISO, and other styles

35

Kurniawan, Rudi, Ahmad Taqwa Martadinata, and Sandy Dwi Cahyo. "Klasifikasi Tingkat Kematangan Buah Sawit Berbasis Deep Learning dengan Menggunakan Arsitektur Yolov5." Journal of Information System Research (JOSH) 5, no. 1 (2023): 302–9. http://dx.doi.org/10.47065/josh.v5i1.4408.

Full text

Abstract:

Object identification and recognition in the field of computer vision is undergoing rapid development and is applied to various fields, ranging from industry to the health sector. This is reflected in the amount of research conducted, including a focus on the application and personalization of machine learning, as well as the development of new models to solve specific problems and challenges. In the palm oil industry, fruit maturity is divided into two categories, namely immature and ripe. Traditionally, fruit maturity is determined visually by experienced workers based on the number of fruit

APA, Harvard, Vancouver, ISO, and other styles

36

JAUD, MELISE, and OLIVIER CADOT. "A second look at the pesticides initiative program: evidence from Senegal." World Trade Review 11, no. 3 (2012): 490–506. http://dx.doi.org/10.1017/s1474745612000286.

Full text

Abstract:

AbstractThis paper investigates whether the Pesticides Initiative Program has significantly affected the export performance of Senegal's horticulture industry. We apply two main microeconometric techniques, difference-in-differences and matching difference-in-differences, to identify the effect of the Pesticides Initiative Program on exports of fresh fruits and vegetables. We use a unique firm-level dataset containing data on sales, employment, and exports by product and destination markets, as well as firm enrolment year, over 2000–2008. The results suggest that while the program had no signi

APA, Harvard, Vancouver, ISO, and other styles

37

FAN, LONG FEI, JIN BAO PU, FANG WU, and YU CHENG DAI. "A new species of Tremella s.s. (Tremellaceae, Basidiomycota) from southeastern China." Phytotaxa 502, no. 2 (2021): 208–16. http://dx.doi.org/10.11646/phytotaxa.502.2.9.

Full text

Abstract:

A new species Tremella zhejiangensis is described from southeastern China based on phenotypic and molecular evidence. The species is characterized by soft gelatinous, yellowish brown to brownish orange, cerebriform basidioma when fresh, thin-walled, globose to subglobose or broadly ellipsoid basidia with a basal clamp connection, globose to subglobose basidiospores measuring 15.0–19.0 × 14.0–17.5 µm with obvious apiculus, and growth on rotten wood of Liquidambar formosana. The phylogeny of Tremella s.s. is constructed using methods of Maximum Parsimony, Maximum Likelihood and Bayesian Inferenc

APA, Harvard, Vancouver, ISO, and other styles

38

Rodrigues, Tatiana Tozzi Martins Souza, Allieksiei Castelar Perim Souza Rodrigues, and Pedro Guilherme Martins Rodrigues. "Soil remineralizer as a replacement for chemical fertilization in industrial tomato cultivation." REVISTA DELOS 18, no. 64 (2025): e3904. https://doi.org/10.55905/rdelosv18.n64-010.

Full text

Abstract:

Tomato is one of the major agricultural crops globally, widely consumed both fresh and processed. However, the high cost of production in Brazil, particularly due to the reliance on imported fertilizers, presents a significant challenge to the production. One alternative for fertilizing crops is the application of soil remineralizers (REM) as a substitute for soluble fertilizers. This study aimed to evaluate the use of Ipirá Fértil® REM in industrial tomato cultivation as a substitute for NPK fertilizers during planting. An experiment was conducted at Fazenda Galheiros in Januária/MG, Brazil,

APA, Harvard, Vancouver, ISO, and other styles

39

Xie, Hong, Yinping Li, Jiaxing Li, et al. "Mycotoxin Determination in Peaches and Peach Products with a Modified QuEChERS Extraction Procedure Coupled with UPLC-MS/MS Analysis." Foods 12, no. 17 (2023): 3216. http://dx.doi.org/10.3390/foods12173216.

Full text

Abstract:

Peaches are the most significant temperate fruit crop worldwide. However, peach fruits are susceptible to fungal and mycotoxin contamination. Consequently, monitoring the residual levels of multiple mycotoxins in peaches and related products is essential. In this study, a novel method based on QuEChERS extraction, followed by ultra-high-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) detection, was developed for analyzing 14 mycotoxins in peaches and peach products from China. Matrix-matched calibrations were employed to accurately quantify the mycotoxins and compensate

APA, Harvard, Vancouver, ISO, and other styles

40

VERINGA, Daniela, Ionel Lucian DUMITRESCU, and Marian BOGOESCU. "The effect of storage conditions on some tomato varieties, during the postharvest period." Notulae Botanicae Horti Agrobotanici Cluj-Napoca 51, no. 1 (2023): 13027. http://dx.doi.org/10.15835/nbha51113027.

Full text

Abstract:

In this study, an analysis was made of how the variety and storage conditions influence the keeping of tomatoes in the fresh conditions, in the post-harvest period. The fruits of three varieties of tomatoes were stored in different technological conditions: at ambient temperature (20-22 °C), with and without air ionization; at a temperature of 10-12 °C, with and without modified atmosphere; and at a temperature of 3-5 °C. The firmness of the pulp and some biochemical components were analysed, respectively the soluble dry matter, the total sugar, the acidity and vitamin C. For the studied varie

APA, Harvard, Vancouver, ISO, and other styles

41

Tanjung, Denny Akbar, and Dewi Nur Anggraeni. "Pelatihan Pembuatan Saus Tomat." Pelita Masyarakat 1, no. 1 (2019): 1–5. http://dx.doi.org/10.31289/pelitamasyarakat.v1i1.2753.

Full text

Abstract:

Sauce is a complementary food, this food is usually coupled with fried foods, soupy foods and can also be used as a seasoning. Sauce made from tomatoes (if tomato sauce) is blended until smooth and cooked while adding spices to make it taste savory. The sauce's delicacy depends on the freshness of the fruit used, the right spice composition and the correct manufacturing procedure. Sauce in the market often does not include the composition of ingredients and food additives (BTM) such as food preservatives used not to mention we do not know the freshness of fruit and BTM doses added and the manu

APA, Harvard, Vancouver, ISO, and other styles

42

Rosalina, Rosalina, Ario Yudo Husodo, and I. Gede Pasek Suta Wijaya. "Development of a Convolutional Neural Network Method for Classifying Ripeness Levels of Servo Variety Tomatoes." Jurnal Teknik Informatika (Jutif) 6, no. 2 (2025): 501–20. https://doi.org/10.52436/1.jutif.2025.6.2.4168.

Full text

Abstract:

The distribution of tomatoes in Indonesia is huge, making it an important commodity in the agricultural sector. However, manual classification of tomato ripeness can lead to human error and decrease supply chain efficiency. Therefore, an automated system capable of classifying tomatoes quickly and accurately is needed, in order to reduce the potential for human error and improve supply chain efficiency. This research aims to develop the Convolutional Neural Network (CNN) method to improve the accuracy of tomato ripeness detection through modifications to the architecture, such as reducing seve

APA, Harvard, Vancouver, ISO, and other styles

43

Anraeni, Siska, Muhid Mustari, Ramdaniah Ramdaniah, Nia Kurniati, and Syahrul Mubarak. "Innovative CNN approach for reliable chicken meat classification in the poultry industry." Bulletin of Social Informatics Theory and Application 8, no. 2 (2024): 226–35. https://doi.org/10.31763/businta.v8i2.686.

Full text

Abstract:

In response to the burgeoning need for advanced object recognition and classification, this research embarks on a journey harnessing the formidable capabilities of Convolutional Neural Networks (CNNs). The central aim of this study revolves around the precise identification and categorization of objects, with a specific focus on the critical task of distinguishing between fresh and spoiled chicken meat. This study's overarching objective is to craft a robust CNN-based classification model that excels in discriminating between objects. In the context of our research, we set out to create a mode

APA, Harvard, Vancouver, ISO, and other styles

44

Pawan Bhambu, Sneha Ramdas Shegar,. "Fruit Quality Analysis and Disease Detection using Deep Learning Techniques." Journal of Electrical Systems 20, no. 3s (2024): 755–62. http://dx.doi.org/10.52783/jes.1372.

Full text

Abstract:

Defective fruits are the main reason for worldwide financial catastrophes in agricultural production. It affects both the dependability and quality of the fruits. Post-harvest, quality checking requires a significant amount of time and labor-intensive skill. Automatically identifying fruit quality enables saving time and labor during harvest. Various algorithms have been created using machine learning and image processing methods to detect and categorize fruit quality. A system using Convolutional Neural Networks (CNN) and transfer learning techniques has been developed to enhance the fruit cl

APA, Harvard, Vancouver, ISO, and other styles

45

Kurniawan, Rudi, Samsuryadi Samsuryadi, Fatma Susilawati Mohamad, Harma Oktafia Lingga Wijaya, and Budi Santoso. "Classification of palm oil fruit ripeness based on AlexNet deep Convolutional Neural Network." SINERGI 29, no. 1 (2025): 207. https://doi.org/10.22441/sinergi.2025.1.019.

Full text

Abstract:

The palm oil industry faces significant challenges in accurately classifying fruit ripeness, which is crucial for optimizing yield, quality, and profitability. Manual methods are slow and prone to errors, leading to inefficiencies and increased costs. Deep Learning, particularly the AlexNet architecture, has succeeded in image classification tasks and offers a promising solution. This study explores the implementation of AlexNet to improve the efficiency and accuracy of palm oil fruit maturity classification, thereby reducing costs and production time. We employed a dataset of 1500 images of p

APA, Harvard, Vancouver, ISO, and other styles

46

Mohammed, Maged, Ramasamy Srinivasagan, Ali Alzahrani, and Nashi K. Alqahtani. "Machine-Learning-Based Spectroscopic Technique for Non-Destructive Estimation of Shelf Life and Quality of Fresh Fruits Packaged under Modified Atmospheres." Sustainability 15, no. 17 (2023): 12871. http://dx.doi.org/10.3390/su151712871.

Full text

Abstract:

The safety and quality of fresh fruits deserve the greatest attention, and are a priority for producers and consumers alike. Modern technologies are crucial to accurately estimating and predicting fresh fruits’ quality and shelf life, to optimize supply chain management. Modified atmosphere packaging (MAP) is an essential method that maintains quality parameters and increases the shelf life of fresh fruits by reducing their ripening rates. This study aimed to develop a cost-effective, non-destructive technique using tiny machine learning (TinyML) and a multispectral sensor to predict/estimate

APA, Harvard, Vancouver, ISO, and other styles

47

Liu, Zhan-Bo, Meng Zhou, Fang Wu, and Jian Yu. "Two New Species of Sidera (Hymenochaetales, Basidiomycota) from Southwest China." Journal of Fungi 8, no. 4 (2022): 385. http://dx.doi.org/10.3390/jof8040385.

Full text

Abstract:

Two new wood-inhabiting fungi, Sidera salmonea sp. Nov. and S. tibetica sp. Nov. in the order Hymenochaetales from southwest China, are described and illustrated based on molecular and morphological evidence. They were found on gymnosperm wood that is rotten and charred. The characteristics of S. salmonea include annual, resupinate basidioma, salmon pores with distinctly white margins, angular pores (7–9 per mm), a dimitic hyphal system, and lunate basidiospores that are 3–3.5 × 0.9–1.1 μm. The characteristics of S. tibetica include annual, resupinate basidioma with a white to cream fresh pore

APA, Harvard, Vancouver, ISO, and other styles

48

Ratri, Arum Andary, Khoirul Umam, Devit Suwardiyanto, Junaedi Adi Prasetyo, and Vivien Arief Wardhany. "Analysis of The Effect of Durian Rind Texture on Sugar Content Using The Box-Counting Method." Applied Technology and Computing Science Journal 6, no. 1 (2023): 22–30. http://dx.doi.org/10.33086/atcsj.v6i1.4222.

Full text

Abstract:

Durian is a popular fruit native to Southeast Asia, even labeled as the king of fruits. Currently, both fresh and processed durians have been very well received by the public in both local and export markets [1]. However, it is not uncommon for durians purchased in poor quality, such as bland taste, rotten, or immature [2]. Therefore, a method to identify the quality of durian is needed. One of the durian elements that can be used to identify its quality is the shape and texture of its outer skin. The object discussed in this paper is the relationship between the fractal dimensions of durian s

APA, Harvard, Vancouver, ISO, and other styles

49

Mutiara Revilianti, Ade Irma Purnamasari, Agus Bahtiar, and Edi Wahyudin. "Application of K-Nearest Neighbor Method for Prediction of Best-Selling Fruit Sales at Ziel Kiosk." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 3 (2025): 1572–78. https://doi.org/10.59934/jaiea.v4i3.952.

Full text

Abstract:

Ziel kiosk sells various types of high-quality fresh fruits. Unfortunately, there is currently no system that manages fruit sales prediction, so there is often a buildup of goods, damaged and rotten goods, or even a shortage of goods, resulting in losses for the kiosk. The data collected is less accurate and effective because the current system is operated manually. This research conducts a data mining process on fruit sales data from Ziel Kiosk from January - December 2023. In sales prediction, Fruit Kiosks can use data mining techniques to be more proactive in managing stock items. This not

APA, Harvard, Vancouver, ISO, and other styles

50

Mauludy, Muh Wildan, Goenawan Brotosaputro, and Mardi Hardjianto. "MEATCHECK: DETEKSI KUALITAS DAGING SAPI BERBASIS MOBILE DEEP LEARNING." Jurnal Ilmiah Informatika Komputer 30, no. 1 (2025): 81–91. https://doi.org/10.35760/ik.2025.v30i1.14265.

Full text

Abstract:

Beef is an important foodstuff that affects consumer satisfaction and market value in the meat industry. The purpose of this research is to develop a model to classify beef quality using the transfer learning method. The data collection method is carried out through taking pictures of beef, which are then labeled based on their quality. Classification uses a transfer learning architecture that can improve the performance of the machine learning model generated for the classification of fresh and rotten meat. The model was tested by looking at accuracy, precision, recall, and f1-score. The resu

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!