Journal articles on the topic 'Fruit quality and grading'
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Sardar, Hassan. "Fruit Quality Estimation by Color for Grading." International Journal of Modeling and Optimization 4, no. 1 (2014): 38–42. http://dx.doi.org/10.7763/ijmo.2014.v4.344.
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Patil, Kavita. "Identifying the Quality of Tomatoes in Image Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 780–82. http://dx.doi.org/10.22214/ijraset.2022.39909.
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Abstract: In agricultural and horticulture. Image processing is one of the widely used application. in this paper automated quality identification using some image processing techniques is there that can be done using some image features which help in quality detection of vegetables like shape, color and size. tomatoes are in high demand because the world population consumes them daily. This research is to improve tomato production and fruit quality through fruit measurement methods, which have a low impact factor on fruit and plant during measurements. As there is high demand for quality fruits in the market fruit grading process is considered as very important. Fruit grading by a human may cause inefficient and it may also leads to some error. Researchers have developed numerous algorithms for quality grading and sorting of fruits. color is most important features for indentifying disease and maturity of the fruit. Here a sorting process is introduced where the image of the fruit is captured and analyzed using image processing techniques and the defected fruit is discarding by this process. the main aim of this paper is to do the quality check of the fruits within a short span of time. Keywords: Fruit grading, Tomato quality, image processing, segmentation, classification
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Utpat, V. B., Dr K. J. e. Karand, and Dr A. O. Mulani. "Grading of Pomegranate Using Quality Analysis." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (February 28, 2022): 875–81. http://dx.doi.org/10.22214/ijraset.2022.40409.
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Abstract: Over the world, India is the largest producer of pomegranate. India produces excellent varieties of pomegranate having soft seeds, very less acids and very attractive colours of fruits and grains. There is tremendous potential for exports of pomegranates from India. Quality grading of pomegranate is an important operation in the export of the pomegranate. Generally external appearance of the fruit decides the quality of the fruit. The fruit with bright colour, good texture and shape is quickly chosen by the customer. Though pomegranate can be qualified or graded manually, it is insufficient method which consumes more time also. Automated grading system quickly grades the pomegranate according to quality of the fruit with no errors. This paper discusses the machine vision approach to form a quality grading analysis system. Keywords: Image acquisition, Image Pre-processing, Image segmentation, and feature extraction, ANN (Artificial Neural Network)
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Zhu, Xueyan, Deyu Shen, Ruipeng Wang, Yili Zheng, Shuchai Su, and Fengjun Chen. "Maturity Grading and Identification of Camellia oleifera Fruit Based on Unsupervised Image Clustering." Foods 11, no. 23 (November 25, 2022): 3800. http://dx.doi.org/10.3390/foods11233800.
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Maturity grading and identification of Camellia oleifera are prerequisites to determining proper harvest maturity windows and safeguarding the yield and quality of Camellia oil. One problem in Camellia oleifera production and research is the worldwide confusion regarding the grading and identification of Camellia oleifera fruit maturity. To solve this problem, a Camellia oleifera fruit maturity grading and identification model based on the unsupervised image clustering model DeepCluster has been developed in the current study. The proposed model includes the following two branches: a maturity grading branch and a maturity identification branch. The proposed model jointly learns the parameters of the maturity grading branch and maturity identification branch and used the maturity clustering assigned from the maturity grading branch as pseudo-labels to update the parameters of the maturity identification branch. The maturity grading experiment was conducted using a training set consisting of 160 Camellia oleifera fruit samples and 2628 Camellia oleifera fruit digital images collected using a smartphone. The proposed model for grading Camellia oleifera fruit samples and images in training set into the following three maturity levels: unripe (47 samples and 883 images), ripe (62 samples and 1005 images), and overripe (51 samples and 740 images). Results suggest that there was a significant difference among the maturity stages graded by the proposed method with respect to seed oil content, seed soluble protein content, seed soluble sugar content, seed starch content, dry seed weight, and moisture content. The maturity identification experiment was conducted using a testing set consisting of 160 Camellia oleifera fruit digital images (50 unripe, 60 ripe, and 50 overripe) collected using a smartphone. According to the results, the overall accuracy of maturity identification for Camellia oleifera fruit was 91.25%. Moreover, a Gradient-weighted Class Activation Mapping (Grad-CAM) visualization analysis reveals that the peel regions, crack regions, and seed regions were the critical regions for Camellia oleifera fruit maturity identification. Our results corroborate a maturity grading and identification application of unsupervised image clustering techniques and are supported by additional physical and quality properties of maturity. The current findings may facilitate the harvesting process of Camellia oleifera fruits, which is especially critical for the improvement of Camellia oil production and quality.
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Saputra, Andri, Wahyu Candra, Yan Soerbakti, Romi Fadli Syahputra, Defrianto Defrianto, and Saktioto Saktioto. "STUDI AWAL GRADING BUAH SAWIT DENGAN BANTUAN INJEKSI TEGANGAN LISTRIK SEARAH." Komunikasi Fisika Indonesia 16, no. 2 (October 31, 2019): 103. http://dx.doi.org/10.31258/jkfi.16.2.103-106.
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Maturity progress of palm fruit is greatly depending on the availability of nutrients and environments. Determining maturity level of palm fruit is important to evaluate the quality of palm oil fruits. The younger or too mature fruits will produce poor quality of crude palm oil (CPO). An appropriate devices are needed that can measure the level of fruit maturity so that uniformity of maturity grade can be carried out to obtain high quality CPO. This research provides a preliminary study of voltage change on the surface of oil palm seeds which subjected by electric potential. The low directional voltage (DC) injection treatment, ~ 10V, was applied to investigate the impact of applied voltage on palm oil seeds with three different levels of maturity, i.e. immature (young), ripe and over ripe . The results shown that oil palm fruit quite quickly responds to injection of DC applied voltage with different responding voltage. This responding voltage tends to increase with increasing maturity levels, but decreases for over ripe fruit which has falling down and starting to dry out.
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Qiao, J., A. Sasao, S. Shibusawa, N. Kondo, and E. Morimoto. "Mobile fruit grading robot : Mapping yield and quality of sweet pepper in real-time." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2004 (2004): 205. http://dx.doi.org/10.1299/jsmermd.2004.205_3.
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Kondo, Naoshi. "Robotization in fruit grading system." Sensing and Instrumentation for Food Quality and Safety 3, no. 1 (December 23, 2008): 81–87. http://dx.doi.org/10.1007/s11694-008-9065-x.
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P., Navitha, Sujatha K., and Beaulah A. "Effect Effect of fruit size on physiological seed quality parameters of Cucumber (Cucumis sativus)." Journal of Applied and Natural Science 11, no. 2 (June 10, 2019): 394–97. http://dx.doi.org/10.31018/jans.v11i2.2046.
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An experiment was carried out at the Department of Seed Science and Technology, Agricultural College and Research Institute, Madurai during 2018 to find out the effect of fruit size on physiological seed quality of cucumber. Variation in fruit size of cucumber results in poor quality seeds. In order to overcome this obstacle fruit grading was done based on weight of fruit to obtain good quality seeds. Harvested fruits of cucumber (Cucumis sativus) were categorized based on the weight into three different groups viz., Big (2.41kg), medium (1.66kg) and small (1.28kg). Observations on fruit and seed quality parameters were recorded. The results revealed that medium sized fruits recorded higher values compared to big and small sized fruits. The number of seeds/fruit recorded higher in medium sized fruit (935 numbers) followed by small (896 numbers) and big (876 numbers) sized fruits. The big, medium and small fruits were recovered to 1.52 %, 1.06% and 0.58% seeds respectively. The physiological quality characters measured in terms of seed germination revealed that seeds of medium sized fruits were recorded higher (80%) followed by seeds of big (82%) and small (65%). The seedling vigour measured through root (17.08cm) and shoot length (14.45cm), dry matter production (0.85g 10 seedlings-1) and vigour index (2522) also proved the superiority in medium sized fruits.
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Leemans, V., M. F. Destain, and H. Magein. "QUALITY FRUIT GRADING BY COLOUR MACHINE VISION: DEFECT RECOGNITION." Acta Horticulturae, no. 517 (March 2000): 405–12. http://dx.doi.org/10.17660/actahortic.2000.517.51.
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Blasco, J., N. Aleixos, and E. Moltó. "Machine Vision System for Automatic Quality Grading of Fruit." Biosystems Engineering 85, no. 4 (August 2003): 415–23. http://dx.doi.org/10.1016/s1537-5110(03)00088-6.
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Phippen, W. B., N. Ozer, A. Hetzroni, J. E. Simon, B. Bordelon, D. J. Charles, P. Angers, G. E. Miles, L. M. Malischke, and D. Trinka. "Electronic Determination of Blueberry Fruit Quality Using Aroma Sensing Technology." HortScience 31, no. 4 (August 1996): 590d—590. http://dx.doi.org/10.21273/hortsci.31.4.590d.
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An electronic sniffer that nondestructively detects aromatic volatiles was used to grade commercially packaged blueberries. A total of 1,358 containers of commercial blueberries entering MBG grading facilities were first “sniffed” using the electronic sniffer, graded by USDA or MGB inspectors, and then subjected to discrimination analyses. The electronic sniffer separated the fresh top grade (grade 1) of fruit from the rest of the grades of blueberries with ≤ 82.79% accuracy when grading into five classes, and ≤89.3% when grading into three quality classes. The sniffer was also able to distinguish hand-harvested fruit from machine-harvested fruit from all cultivars tested (Bluecrop, Jersey, and Elliot). Highest classification accuracy was achieved with four gas sensors operating simultaneously within the sniffer. A stable signal response was achieved in 10 seconds, with each berry pack sampled at 10, 20, 40 and 80 seconds.
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Hemamalini, V., S. Rajarajeswari, S. Nachiyappan, M. Sambath, T. Devi, Bhupesh Kumar Singh, and Abhishek Raghuvanshi. "Food Quality Inspection and Grading Using Efficient Image Segmentation and Machine Learning-Based System." Journal of Food Quality 2022 (February 11, 2022): 1–6. http://dx.doi.org/10.1155/2022/5262294.
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One of the most critical aspects of quality assurance is inspecting products for defects before they are sold or shipped. A good product is more vital than having more of the same item for a customer’s enjoyment. The client has a significant role in determining the quality of a product. Another way to think about quality is as the total of all the characteristics that contribute to the creation of items that the client enjoys. Recently, the application of machine vision and image processing technology to improve the surface quality of fruits and other foods has increased significantly. This is primarily because these technologies make significant advancements in areas where the human eye falls short. This means that, by utilizing computer vision and image processing techniques, time-consuming and subjective industrial quality control processes can be eliminated. This article discusses how to check and assess food using picture segmentation and machine learning. It is capable of classifying fruits and determining whether a piece of fruit is rotten. To begin, Gaussian elimination is used to remove noise from images. Then, photos are subjected to histogram equalization in order to improve their quality. Segmentation of the image is carried out using the K-means clustering technique. Then, fruit photos are classified using machine learning methods such as KNN, SVM, and C4.5. These algorithms determine if a fruit is damaged or not.
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Fitri, Zilvanhisna Emka, Ari Baskara, Abdul Madjid, and Arizal Mujibtamala Nanda Imron. "Comparison of Classification for Grading Red Dragon Fruit (<i>Hylocereus Costaricensis</i>)." JURNAL NASIONAL TEKNIK ELEKTRO 11, no. 1 (March 29, 2022): 43–49. http://dx.doi.org/10.25077/jnte.v11n1.899.2022.
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Pitaya is another name for dragon fruit which is currently a popular fruit, especially in Indonesia. One of the problems related to determining the quality of dragon fruit is the postharvest sorting and grading process. In general, farmers determine the grading system by measuring the weight or just looking at the size of the fruit, of course, this raises differences in grading perceptions so that it is not by SNI. This research is a development of previous research, but we changed the type of dragon fruit from white dragon fruit (Hylocereus undatus) to red dragon fruit (Hylocereus costaricensis). We also adapted the image processing and classification methods in previous studies and then compared them with other classification methods. The number of images in the training data is 216, and the number of images in the testing data is 75. The comparison of the accuracy of the three classification methods is 84% for the KNN method, 85.33% for the Naive Bayes method, and 86.67% for the Backpropagation method. So that the backpropagation method is the best classification method in classifying the quality grading of red dragon fruit. The network architecture used is 4, 8, 3 with a learning rate of 0.3 so that the training accuracy is 98.61% and the testing accuracy is 86.67%.
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Li, Jiang Bo, Xiu Qin Rao, and Yi Bin Ying. "Inspection and Grading of Surface Defects of Fruits by Computer Vision." Advanced Materials Research 317-319 (August 2011): 956–61. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.956.
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Computer vision is a rapid, consistent and objective inspection technique, which has expanded into many diverse industries. Its speed and accuracy provide one alternative for an automated, non-destructive and cost-effective technique to accomplish ever-increasing production and quality requirements. This method of inspection has found applications in the agricultural industry, including the inspection and grading of fruits. This paper provides an introduction to main defection and grading approaches of fruit external defects, including image processing and pattern recognition methods based on fruit two-dimensional (2D) and three-dimensional (3D) information, and hyperspectral and multispectral imaging. In addition, their advantages and disadvantages are also discussed.
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Qiao, Jun, Akira Sasao, Sakae Shibusawa, Naoshi Kondo, and Eiji Morimoto. "Mapping Yield and Quality using the Mobile Fruit Grading Robot." Biosystems Engineering 90, no. 2 (February 2005): 135–42. http://dx.doi.org/10.1016/j.biosystemseng.2004.10.002.
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Baietto, Manuela, and Alphus Wilson. "Electronic-Nose Applications for Fruit Identification, Ripeness and Quality Grading." Sensors 15, no. 1 (January 6, 2015): 899–931. http://dx.doi.org/10.3390/s150100899.
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Zhang, Guoxiang, Qiqi Fu, Zetian Fu, Xinxing Li, Maja Matetić, Marija Brkic Bakaric, and Tomislav Jemrić. "A Comprehensive Peach Fruit Quality Evaluation Method for Grading and Consumption." Applied Sciences 10, no. 4 (February 17, 2020): 1348. http://dx.doi.org/10.3390/app10041348.
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Peaches are a popular fruit appreciated by consumers due to their eating quality. Quality evaluation of peaches is important for their processing, inventory control, and marketing. Eleven quality indicators (shape index, volume, mass, density, firmness, color, impedance, phase angle, soluble solid concentration, titratable acidity, and sugar–acid ratio) of 200 peach fruits (Prunus persica (L.) Batsch “Spring Belle”) were measured within 48 h. Quality indicator data were normalized, outliers were excluded, and correlation analysis showed that the correlation coefficients between dielectric properties and firmness were the highest. A back propagation (BP) neural network was used to predict the firmness of fresh peaches based on their dielectric properties, with an overall fitting ratio of 86.9%. The results of principal component analysis indicated that the cumulative variance of the first five principal components was 85%. Based on k-means clustering analysis, normalized data from eleven quality indicators in 190 peaches were classified into five clusters. The proportion of red surface area was shown to be a poor basis for picking fresh peaches for the consumer market, as it bore little relationship with the comprehensive quality scores calculated using the new grading model.
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S, Raghavendra, Souvik Ganguli, P. Thirumarai Selvan, Mitali Madhusmita Nayak, Sushovan Chaudhury, Randell U. Espina, and Isaac Ofori. "Deep Learning Based Dual Channel Banana Grading System Using Convolution Neural Network." Journal of Food Quality 2022 (May 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/6050284.
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Deep learning has recently been hailed as the most advanced computer vision technology for image classification. The invention of convolutional neural network (CNN) simplified the effort of feature engineering. Classification of various stages of fruit maturity using machine learning algorithms is a difficult task since it is difficult to distinguish the visual features of the fruits at different maturity stages. Fruit ripeness is critical in agriculture since it impacts the quality of the fruit. Manually determining the maturity of the fruit has various flaws, including the fact that it takes a long time, needs a lot of labor, and can lead to inconsistencies. In developing countries, agriculture is one of the most important economic sectors. Created system can be employed in the food processing business, in real-life applications where the intelligent system’s accuracy, cost, and speed will improve the production rate and allow satisfying consumer demand. With small number of image samples, the system is capable of automating assembly line related work for classifying bananas along with sufficient overall accuracy. The noninvasive method will also be used to classify other clustered fruits or horticultural crops in the future. The system can either replace or aid human operators who can focus their efforts on fruit selection. The combined merits of RGB and HSI (hyperspectral imaging) for classification of bananas were highlighted in the present study; they have possible application as a model for classification of several types of horticultural produce. The multi-input model’s quick processing time can be a useful and handy technique in the farm field during postharvest procedures. Via a combination of CNN and MLP applied to data collected using RGB and hyperspectral imaging, the multi-input model reliably recognizes bananas with an accuracy level of 98.4 percent as well as an F1-score of 0.97. The AI algorithm predicted the size (large, medium, and microscopic) and perspective (front or rear half) of banana classes with 99 percent accuracy. In comparison to previous studies that simply employed RGB imaging, the presented model revealed the value of integrating RGB imaging and HSI approaches.
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Hogmire, H. W., T. A. Baugher, M. Ingle, and G. W. Lightner. "Development of a Sampling Plan and Application of a Grading Scheme for Determining Apple Packout Losses." HortScience 24, no. 4 (August 1989): 628–30. http://dx.doi.org/10.21273/hortsci.24.4.628.
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Abstract A sampling plan was developed and used along with a modified grading scheme as a tool to predict apple (Malus domestica, Borkh.) fruit quality, thus providing a means to evaluate the impact of orchard management practices on market potential. Apple extra fancy/fancy packout was predicted to within 10% by examining a 100-fruit sample from each of five bins at the submersion tank. Packout loss factors were predicted to within 5% by sampling 100 fruit from each of four bins. A modified Russo/Rajotte grading scheme in chart format proved to be a useful tool for assessing packout losses. An evaluation of downgraded fruit, comparing the grading scheme to grower practice, yielded coefficients of determination ranging from 0.83 to 0.94 for five of six fruit lots sampled. The grower’s marketing intentions and the tendency of packinghouse staff to give more attention to the most obvious defects during grading influenced the ability to predict packout and the severity of loss factors.
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Nithya, R., B. Santhi, R. Manikandan, Masoumeh Rahimi, and Amir H. Gandomi. "Computer Vision System for Mango Fruit Defect Detection Using Deep Convolutional Neural Network." Foods 11, no. 21 (November 2, 2022): 3483. http://dx.doi.org/10.3390/foods11213483.
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Machine learning techniques play a significant role in agricultural applications for computerized grading and quality evaluation of fruits. In the agricultural domain, automation improves the quality, productivity, and economic growth of a country. The quality grading of fruits is an essential measure in the export market, especially defect detection of a fruit’s surface. This is especially pertinent for mangoes, which are highly popular in India. However, the manual grading of mango is a time-consuming, inconsistent, and subjective process. Therefore, a computer-assisted grading system has been developed for defect detection in mangoes. Recently, machine learning techniques, such as the deep learning method, have been used to achieve efficient classification results in digital image classification. Specifically, the convolution neural network (CNN) is a deep learning technique that is employed for automated defect detection in mangoes. This study proposes a computer-vision system, which employs CNN, for the classification of quality mangoes. After training and testing the system using a publicly available mango database, the experimental results show that the proposed method acquired an accuracy of 98%.
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Lee, Dah-Jye, James K. Archibald, and Guangming Xiong. "Rapid Color Grading for Fruit Quality Evaluation Using Direct Color Mapping." IEEE Transactions on Automation Science and Engineering 8, no. 2 (April 2011): 292–302. http://dx.doi.org/10.1109/tase.2010.2087325.
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Wang, Yawei, and Yifei Chen. "Fruit Morphological Measurement Based on Three-Dimensional Reconstruction." Agronomy 10, no. 4 (March 25, 2020): 455. http://dx.doi.org/10.3390/agronomy10040455.
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Three-dimensional (3D) shape information is valuable for fruit quality evaluation. Grading of the fruits is one of the important postharvest tasks that the fruit processing agro-industries do. Although the internal quality of the fruit is important, the external quality of the fruit influences the consumers and the market price significantly. To solve the problem of feature size extraction in 3D fruit scanning, this paper proposes an automatic fruit measurement scheme based on a 2.5-dimensional point cloud with a Kinect depth camera. For getting a complete fruit model, not only the surface point cloud is obtained, but also the bottom point cloud is rotated to the same coordinate system, and the whole fruit model is obtained by iterative closest point algorithm. According to the centroid and principal direction of the fruit, the cut plane of the fruit is made in the x-axis, y-axis, and z-axis respectively to obtain the contour line of the fruit. The experiment is divided into two groups, the first group is various sizes of pears to get the morphological parameters; the second group is the various colors, shapes, and textures of many fruits to get the morphological parameters. Comparing the predicted value with the actual value shows that the automatic extraction scheme of the size information is effective and the methods are universal and provide a reference for the development of the related application.
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Dr. K. Sarojini, S. Kavitha,. "DE-NOISING OF TOMATO FRUIT IMAGE USING SPIRAL SEED FILTER." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 5, 2021): 505–11. http://dx.doi.org/10.17762/itii.v9i1.163.
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Fruit disease causes more economic losses in agricultural industry. In prediction of disease image pre-processing plays an important role. Fruits may appear healthy and fresh to human eye but its quality is known by customer after eating the fruits. Images are used to forecast quality of the fruits and vegetables, but accuracy of grading will be affected by distortion. Various noise affect the quality of the image and it can be denoised by various filters. The preservative edges, background information and contrast of images are the challenging issues in exiting filtering methods. This research proposed Spiral Seed Filter (SSF) to increase the quality of the tomato fruit image by extracting the luma variance and by applying the row wise and column wise 3x3 cross correlation. The result shows that the proposed filter increases the PSNR (Peak Signal to Noise ratio) and reduces MSE (Mean Square Error) metric values and yield good results. It gives highest PSNR value such as 94.68. It gives 0.0001 as MSE value for proposed method.
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Park, Yu-Jin, Duk-Hee Cha, Ki-yun Jung, Bong-Hwa Kang, and Jung-Myung Lee. "(31) Quality Changes in Oriental Melon as Affected by Washing after Fruit Harvest and 1-MCP Treatment." HortScience 40, no. 4 (July 2005): 1004C—1004. http://dx.doi.org/10.21273/hortsci.40.4.1004c.
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Washing oriental melon (Cucumismelo var. makuwa Makino) is a standard procedure because it facilitates the precise elimination of defective fruit, such as fruit having internal decay symptoms, and also facilitates easier handling of fruit by the elimination of gummy substances on the fruit surface. In most fresh fruits and vegetables, however, washing has never been recommended unless it is related to other practices, such as waxing or immediate processing. Harvested oriental melons were placed in a big water tank and washed with a brush machine immediately before grading, using an automatic grader. Fruit that had sunk down to the bottom of the tank were discarded, as they were premature-fermented fruit with no commercial value. Fruit, intact or washed, were treated with 1-MCP at 0.5–2.0 ppm for 12 hours and stored at room temperature for 3 weeks. Flesh firmness, soluble solids contents, fruit petiole color, and changes of surface suture color were measured to evaluate storability of the fruit. The washed fruit exhibited poor skin color and early suture-browning as compared to the non-washed fruit, regardless of 1-MCP pretreatment. 1-MCP treatment was also effective in maintaining fresh fruit quality as compared to the non-treated fruit. 1-MCP effects were, however, more pronounced in relatively smaller and less mature fruit as compared to the fully mature fruit. 1-MCP was also effective in maintaining white suture color, the most important visual factor currently used for quality evaluation in oriental melon.
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Misron, Norhisam, Nisa Syakirah Kamal Azhar, Mohd Nizar Hamidon, Ishak Aris, Kunihisa Tashiro, and Hirokazu Nagata. "Fruit Battery with Charging Concept for Oil Palm Maturity Sensor." Sensors 20, no. 1 (December 31, 2019): 226. http://dx.doi.org/10.3390/s20010226.
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There are many factors affecting oil extraction rate (OER) but a large contributor to high national OER is by processing good-quality fresh fruit bunches (FFB) at the mills. The current practice for grading oil palm fruit bunches in mills is using human graders for visual inspection, which can lead to repeated mistakes, inconsistent evaluation results, and many other related losses. This study aims to develop a fruit maturity sensor that can detect oil palm fruit maturity grade and send indication to the user whether to accept or reject the bunches. This study focuses on fruit battery principle and applying the charging concept to the fruit battery in order to generate significant load voltage readings of oil palm fruit battery. The charging process resulted in amplified load voltage readings, which were 4 times more sensitive to changes as compared to normal fruit battery without charging process. From the load voltage readings, the fruits can be characterized into their maturity grade based on moisture content. It was determined that fruits with moisture content less than 44% and average load voltage, Vavg, between 20 to 30 mV are considered ripe fruits.
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Patkar, Gaurang S., Anjaneyulu G.S.G.N, and Chandra Mouli P.V.S.S.R. "Challenging Issues in Automated Oil Palm Fruit Grading." IAES International Journal of Artificial Intelligence (IJ-AI) 7, no. 3 (August 6, 2018): 111. http://dx.doi.org/10.11591/ijai.v7.i3.pp111-118.
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<span lang="TR">Late advancement in Agriculture segment utilizing Image preparing and fuzzy logic methods has empowered ranchers to expand the yield of harvest and served the nourishment needs of the whole people. Look into in horticulture is pointed towards increment in the profitability, quality and lessening the likelihood of blunder presented by people. The biggest oil palm creation is in Malaysia and Indonesia and they send out palm oil to different nations on the planet. The most outrageous enthusiasm for palm oil is in India. This came to fruition India into Palm Oil advancement and era in various states . With a specific end goal to expand the efficiency of palm oil organic products, palm oil industry and in addition analysts utilizes different machine-vision systems to review the natural products. Tragically, the information caught and prepared is confronted with restricted learning and accuracy. There are a few difficulties required with the outline and usage of palm oil organic product reviewing. This paper introduces an outline of different Image handling and fuzzy logic methods, distinguishes and addresses testing issues in computerized palm natural product evaluating.</span>
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Mesa, Armacheska Rivero, and John Y. Chiang. "Multi-Input Deep Learning Model with RGB and Hyperspectral Imaging for Banana Grading." Agriculture 11, no. 8 (July 21, 2021): 687. http://dx.doi.org/10.3390/agriculture11080687.
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Grading is a vital process during the postharvest of horticultural products as it dramatically affects consumer preference and satisfaction when goods reach the market. Manual grading is time-consuming, uneconomical, and potentially destructive. A non-invasive automated system for export-quality banana tiers was developed, which utilized RGB, hyperspectral imaging, and deep learning techniques. A real dataset of pre-classified banana tiers based on quality and size (Class 1 for export quality bananas, Class 2 for the local market, and Class 3 for defective fruits) was utilized using international standards. The multi-input model achieved an excellent overall accuracy of 98.45% using only a minimal number of samples compared to other methods in the literature. The model was able to incorporate both external and internal properties of the fruit. The size of the banana was used as a feature for grade classification as well as other morphological features using RGB imaging, while reflectance values that offer valuable information and have shown a high correlation with the internal features of fruits were obtained through hyperspectral imaging. This study highlighted the combined strengths of RGB and hyperspectral imaging in grading bananas, and this can serve as a paradigm for grading other horticultural crops. The fast-processing time of the multi-input model developed can be advantageous when it comes to actual farm postharvest processes.
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Abu Bakar, M. N., A. H. Abdullah, N. Abdul Rahim, H. Yazid, N. S. Zakaria, S. Omar, W. M. F. Wan Nik, et al. "Defects Detection Algorithm of Harumanis Mango for Quality Assessment Using Colour Features Extraction." Journal of Physics: Conference Series 2107, no. 1 (November 1, 2021): 012008. http://dx.doi.org/10.1088/1742-6596/2107/1/012008.
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Abstract Visual defects detection is one of the main problems in the post-harvest processing caused a major production and economic losses in agricultural industry. Manual fruits detection become easy when it is done in small amount, but the result is not consistent which will generate issue in fruit grading. A new fruit quality assessment system is necessary in order to increase the accuracy of classification, more consistencies, efficient and cost effective that would enable the industry to grow accordingly. In this paper, a method based on colour feature extraction for the quality assessment of Harumanis mango is proposed and experimentally validated. This method, including image background removal, defects segmentation and recognition and finally quality classification using Support Vector Machine (SVM) was developed. The results show that the experimental hardware system is practical and feasible, and that the proposed algorithm of defects detection is effective.
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Benady, Meny, Amots Hetzroni, James E. Simon, and Bruce Bordelon. "235 ELECTRONIC SENSING OF AROMATIC VOLATILE!? FOR QUALITY SORTING IN BLUEBERRIES." HortScience 29, no. 5 (May 1994): 463b—463. http://dx.doi.org/10.21273/hortsci.29.5.463b.
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We have developed an electronic sensor (“sniffer”) that measures fruit ripeness rapidly and nondestructively by measuring the aromatic volatiles that are naturally emitted by ripening fruit. In this study, we evaluated the potential of using the fruit ripeness sniffer in the quality sorting of blueberries. Blueberries were first visually classified into four distinct ripeness classes: unripe; half-ripe; ripe; and over-ripe and quantitatively measured for color, firmness, TSS, and sugar acid ratio. Ripeness classification accuracy with the sniffer matched or exceeded that of all other ripeness indices. The sniffer differentiated unripe, ripe and over-ripe berries within one second, but could not distinguish between the unripe and half-ripe class. Detection of l-2 damaged or 1-2 soft fruit spiked within a large container of 24-37 high quality ripe fruit was also achieved, but required a response time of 10 seconds. Electronic sensing of aromatic volatiles may be a useful new technique in the grading and sorting of blueberries.
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Dhiman, Poonam. "Contemporary Study on Citrus Disease Classification System." ECS Transactions 107, no. 1 (April 24, 2022): 10035–43. http://dx.doi.org/10.1149/10701.10035ecst.
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Machine vision systems enable many applications in all important fields of life like medical healthcare, agriculture, fruit and vegetable industry, etc. One of the application fields is disease detection of fruit. The disease identification of fruits is a critical issue and advanced automatic detection systems need to be developed. In the recent years, image processing techniques have been employed for the quality evaluation of the fruits. This paper presents the current advancement in image processing techniques used by the disease recognition system of the citrus fruits. In past few years, different approaches are applied for grading the citrus fruits using machine vision system. The paper presents the overview of different techniques like pre-processing, segmentation, and classification that is used by the disease detection system of citrus fruits. This paper also presents the detailed description of the different state of art disease detection system proposed by the researcher for attending identifying the disease present in citrus fruit. The detailed survey of the disease detection technique present in citrus fruit has been presented to investigate the usage of recent approaches employed in machine vision systems.
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Okere, Emmanuel Ekene, Ebrahiema Arendse, Alemayehu Ambaw Tsige, Willem Jacobus Perold, and Umezuruike Linus Opara. "Pomegranate Quality Evaluation Using Non-Destructive Approaches: A Review." Agriculture 12, no. 12 (November 28, 2022): 2034. http://dx.doi.org/10.3390/agriculture12122034.
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Pomegranate (Punica granatum L.) is one of the most healthful and popular fruits in the world. The increasing demand for pomegranate has resulted in it being processed into different food products and food supplements. Researchers over the years have shown interest in exploring non-destructive techniques as alternative approaches for quality assessment of the harvest at the on-farm point to the retail level. The approaches of non-destructive techniques are more efficient, inexpensive, faster and yield more accurate results. This paper provides a comprehensive review of recent applications of non-destructive technology for the quality evaluation of pomegranate fruit. Future trends and challenges of using non-destructive techniques for quality evaluation are highlighted in this review paper. Some of the highlighted techniques include computer vision, imaging-based approaches, spectroscopy-based approaches, the electronic nose and the hyperspectral imaging technique. Our findings show that most of the applications are focused on the grading of pomegranate fruit using machine vision systems and the electronic nose. Measurements of total soluble solids (TSS), titratable acidity (TA) and pH as well as other phytochemical quality attributes have also been reported. Value-added products of pomegranate fruit such as fresh-cut and dried arils, pomegranate juice and pomegranate seed oil have been non-destructively investigated for their numerous quality attributes. This information is expected to be useful not only for those in the grower/processing industries but also for other agro-food commodities.
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Hosainpour, Adel, Kamran Kheiralipour, Mohammad Nadimi, and Jitendra Paliwal. "Quality Assessment of Dried White Mulberry (Morus alba L.) Using Machine Vision." Horticulturae 8, no. 11 (November 1, 2022): 1011. http://dx.doi.org/10.3390/horticulturae8111011.
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Over the past decade, the fresh white mulberry (Morus alba L.) fruit has gained growing interest due to its superior health and nutritional characteristics. While white mulberry is consumed as fresh fruit in several countries, it is also popular in dried form as a healthy snack food. One of the main challenges that have prevented a wider consumer uptake of this nutritious fruit is the non-uniformity in its quality grading. Therefore, identifying a reliable quality grading tool can greatly benefit the relevant stakeholders. The present research addresses this need by developing a novel machine vision system that combines the key strengths of image processing and artificial intelligence. Two grades (i.e., high- and low-quality) of white mulberry were imaged using a digital camera and 285 colour and textural features were extracted from their RGB images. Using the quadratic sequential feature selection method, a subset of 23 optimum features was identified to classify samples into two grades using artificial neural networks (ANN) and support vector machine (SVM) classifiers. The developed system under both classifiers achieved the highest correct classification rate (CCR) of 100%. Indeed, the latter approach offered a smaller mean squared error for the training and test sets. The developed model’s high accuracy confirms the machine vision’s suitability as a reliable, low-cost, rapid, and intelligent tool for quality monitoring of dried white mulberry.
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Forney, Charles F. "Postharvest Handling and Storage of Fresh Cranberries." HortTechnology 13, no. 2 (January 2003): 267–72. http://dx.doi.org/10.21273/horttech.13.2.0267.
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High-quality cranberry (Vaccinium macrocarpon) fruit are required to fulfil the growing markets for fresh fruit. Storage losses of fresh cranberries are primarily the result of decay and physiological breakdown. Maximizing quality and storage life of fresh cranberries starts in the field with good cultural practices. Proper fertility, pest management, pruning, and sanitation all contribute to the quality and longevity of the fruit. Mechanical damage in the form of bruising must be minimized during harvesting and postharvest handling, including storage, grading, and packaging. In addition, water-harvested fruit should be removed promptly from the bog water. Following harvest, fruit should be cooled quickly to an optimum storage temperature of between 2 and 5 °C (35.6 and 41.0 °F). The development of improved handling, refined storage conditions, and new postharvest treatments hold promise to extend the storage life of fresh cranberries.
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Bhargava, Anuja, and Atul Bansal. "Classification and Grading of Multiple Varieties of Apple Fruit." Food Analytical Methods 14, no. 7 (February 6, 2021): 1359–68. http://dx.doi.org/10.1007/s12161-021-01970-0.
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Tang, Chun Xiao, En Bang Li, Chuan Zhen Zhao, and Chao Li. "Quality Detection and Specie Identification of Apples Based on Multi-Spectral Imaging." Advanced Materials Research 301-303 (July 2011): 158–64. http://dx.doi.org/10.4028/www.scientific.net/amr.301-303.158.
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This paper introduced an apple quality detection and specie identification system based on multi-spectral imaging. Under an international mixed light illumining, system can capture red, green and infrared images of apples at the same time. A software programmed based on Matlab 6.5.1 is used for image processing to complete the detection of quality and specie. According to processing results, the subtotals and classification are made into grading standards. These can be quickly and easily applied to the automation of agriculture fruit grading system. In the experiment, some most common apples including Fuji apple, Red delicious apples, Green apples, Gina Apple's were detected for quality and variety . Accuracy rate can be more than 90%.
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Arakeri, Megha P., and Lakshmana. "Computer Vision Based Fruit Grading System for Quality Evaluation of Tomato in Agriculture industry." Procedia Computer Science 79 (2016): 426–33. http://dx.doi.org/10.1016/j.procs.2016.03.055.
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Liang, Xiaoting, Xueying Jia, Wenqian Huang, Xin He, Lianjie Li, Shuxiang Fan, Jiangbo Li, Chunjiang Zhao, and Chi Zhang. "Real-Time Grading of Defect Apples Using Semantic Segmentation Combination with a Pruned YOLO V4 Network." Foods 11, no. 19 (October 10, 2022): 3150. http://dx.doi.org/10.3390/foods11193150.
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At present, the apple grading system usually conveys apples by a belt or rollers. This usually leads to low hardness or expensive fruits being bruised, resulting in economic losses. In order to realize real-time detection and classification of high-quality apples, separate fruit trays were designed to convey apples and used to prevent apples from being bruised during image acquisition. A semantic segmentation method based on the BiSeNet V2 deep learning network was proposed to segment the defective parts of defective apples. BiSeNet V2 for apple defect detection obtained a slightly better result in MPA with a value of 99.66%, which was 0.14 and 0.19 percentage points higher than DAnet and Unet, respectively. A model pruning method was used to optimize the structure of the YOLO V4 network. The detection accuracy of defect regions in apple images was further improved by the pruned YOLO V4 network. Then, a surface mapping method between the defect area in apple images and the actual defect area was proposed to accurately calculate the defect area. Finally, apples on separate fruit trays were sorted according to the number and area of defects in the apple images. The experimental results showed that the average accuracy of apple classification was 92.42%, and the F1 score was 94.31. In commercial separate fruit tray grading and sorting machines, it has great application potential.
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McCormick, Roy, and Konni Biegert. "Monitoring the growth and maturation of apple fruit on the tree with handheld Vis/NIR devices." NIR news 30, no. 1 (November 21, 2018): 12–15. http://dx.doi.org/10.1177/0960336018814147.
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Modern computer-controlled fruit grading and sorting facilities, with non-destructive Vis/NIR technologies based on transmission spectroscopy are now common. The main applications, at least in apple sorting, are not only to identify internal browning disorders in fruit after storage but also to grade out fruit with physiological conditions like watercore (fluid filled, glassy looking areas within the fruit flesh) in the period after harvest but before long-term storage. Usually, both these types of internal flesh disorders are not visible from the outside of the apple; thus, high-speed Vis/NIR non-destructive sorting of the individual fruit has been a considerable advancement for the fruit industry to ensure the supply of good quality apples to the end consumer.
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Prahudaya, Taftyani Yusuf, and Agus Harjoko. "METODE KLASIFIKASI MUTU JAMBU BIJI MENGGUNAKAN KNN BERDASARKAN FITUR WARNA DAN TEKSTUR." Jurnal Teknosains 6, no. 2 (August 30, 2017): 113. http://dx.doi.org/10.22146/teknosains.26972.
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Guava (Psidium guajava L.) is a fruit that has many health benefits. Guava also has commercial value in Indonesia and has a large market share. This indicates that the commodity of guava has been consumed by society extensively. This time the sorting process is still done manually which still has many shortcomings. This classification gives the classification results are less accurate and inconsistent due to the carelessness of humans. Grading process in the marketing sector is essential. Improper grading potentially detrimental to farmers because all the fruit quality were priced the same. Therefore, we need a consistent classification system.The system uses image processing to extract the color and texture features of guava. As a quality classification KNN method (K-Nearest Neighbor) is used. This system will classify guava into four quality classes, namely the super class, class A, class B, and external quality. KNN designed with input 7 features extraction which is the average value of RGB (Red, Green, and Blue), total defect area, and the GLCM value (entropy, homogeneity, and contrast) with the 4 outputs of quality. From the test results showed that the classification method is able to classify the quality of guava. The highest accuracy is obtained in testing K = 3 with 91.25% accuracy rate.
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NeSmith, D. Scott, Stanley E. Prussia, and Gerard Krewer. "Firmness of `Brightwell' Rabbiteye Blueberry in Response to Various Harvesting and Handling Procedures." HortScience 35, no. 4 (July 2000): 560F—561. http://dx.doi.org/10.21273/hortsci.35.4.560f.
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Experiments were conducted during 1999 at the Univ. of Georgia Research Farm near Alapaha with the rabbiteye blueberry (Vaccinium ashei Reade) cultivar Brightwell to determine how various harvesting and handling tactics influenced firmness. The research was facilitated by availability of a mechanical harvester and a commercial packing line. Firmness was determined with a FirmTech II firmness tester on fruit samples before and after cold storage. Fruit harvesting methods included machine harvesting in bulk, hand harvesting in bulk, and hand harvesting directly into clam shell containers. Assessment of precooling effects were made by comparing firmness of fruit that were placed immediately over ice after harvest to fruit that remained at ambient temperatures for 24 hours after harvest. Additional measurements were made to discern the effects of grading and sorting on fruit firmness. The data overall indicated that `Brightwell' fruit firmness was “acceptable” regardless of the harvesting and handling methods experienced. However, there were considerable firmness losses caused by the various procedures. The greatest loss in fruit firmness (20% to 25%) was caused by machine harvesting. This was followed by a 15% to 18% loss of firmness due to grading and sorting. Immediate cooling of fruit after harvest resulted in only a 8% to 12% increase in firmness as compared to keeping fruit at ambient temperature for 24 hours. These findings should be useful to growers and packers in targeting segments of their operations that can be manipulated to improve berry firmness and quality for fresh market sales.
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Obenland, David, Dennis Margosan, Joseph L. Smilanick, and Bruce Mackey. "Ultraviolet Fluorescence to Identify Navel Oranges with Poor Peel Quality and Decay." HortTechnology 20, no. 6 (December 2010): 991–95. http://dx.doi.org/10.21273/hortsci.20.6.991.
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Navel oranges (Citrus sinensis) were sorted into four groups under ultraviolet illumination in commercial packinghouse black light rooms based upon the amount of fluorescence visible on each fruit to determine if fluorescence was predictive of peel quality. The groups corresponded to fruit with 1) little or no fluorescence (group 0), 2) low fluorescence (group 1), 3) moderate fluorescence (group 2), and 4) large fluorescent areas (group 3) that were indicative of developing decay lesions. Identification and elimination of group 3 fruit in black light rooms is a common practice now, but the other groups pass through these rooms. Six tests were conducted over a 2-year period during different times in the mid to late navel orange season. Fruit were visually evaluated for peel quality within 24 hours of their initial segregation into fluorescence groups and again following 3 weeks of storage at 15 °C. Peel quality assessment was based upon commercial grading practices, and the fruit were placed into fancy, choice, juice, or decay classes. Fruit with low to no peel fluorescence (groups 0 and 1) had numerous fancy-grade fruit and few juice- and decay-grade fruit in comparison with the other two groups. In contrast, fruit with moderate fluorescence (group 2) were of poor peel quality. In the initial evaluation, this group had 28% fewer fancy fruit and 19% more juice fruit than did group 0. During storage, group 2 fruit declined markedly in quality and numerous fruit of group 2 in the choice and juice classes decayed; the percentage of decayed fruit increased from 1% initially to 29% after 3 weeks of storage. Navel oranges in group 3, with numerous and obvious fluorescent decay lesions, mainly consisted of either juice grade or decayed fruit; 70% of group 3 decayed after 3 weeks. In addition to removing fluorescing fruit that have obvious indications of decay (group 3), it would be advantageous to remove or otherwise recognize that fruit with moderate levels of fluorescence (group 2) are also of lower quality and that they should not be selected for long storage or distant transport. Their identification may be most practical with an automated system using machine vision and ultraviolet illumination.
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Guthrie, J., and K. Walsh. "Non-invasive assessment of pineapple and mango fruit quality using near infra-red spectroscopy." Australian Journal of Experimental Agriculture 37, no. 2 (1997): 253. http://dx.doi.org/10.1071/ea96026.
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Summary. The potential of near infra-red (NIR) spectroscopy for non-invasive measurement of fruit quality of pineapple (Ananas comosus var. Smooth Cayenne) and mango (Magnifera indica var. Kensington) fruit was assessed. A remote reflectance fibre optic probe, placed in contact with the fruit skin surface in a light-proof box, was used to deliver monochromatic light to the fruit, and to collect NIR reflectance spectra (760–2500 nm). The probe illuminated and collected reflected radiation from an area of about 16 cm2. The NIR spectral attributes were correlated with pineapple juice Brix and with mango flesh dry matter (DM) measured from fruit flesh directly underlying the scanned area. The highest correlations for both fruit were found using the second derivative of the spectra (d2 log 1/R) and an additive calibration equation. Multiple linear regression (MLR) on pineapple fruit spectra (n = 85) gave a calibration equation using d2 log 1/R at wavelengths of 866, 760, 1232 and 832 nm with a multiple coefficient of determination (R2) of 0.75, and a standard error of calibration (SEC) of 1.21 °Brix. Modified partial least squares (MPLS) regression analysis yielded a calibration equation with R2 = 0.91, SEC = 0.69, and a standard error of cross validation (SECV) of 1.09 oBrix. For mango, MLR gave a calibration equation using d2 log 1/R at 904, 872, 1660 and 1516 nm with R2 = 0.90, and SEC = 0.85% DM and a bias of 0.39. Using MPLS analysis, a calibration equation with R2 = 0.98, SEC = 0.54 and SECV = 1.19 was obtained. We conclude that NIR technology offers the potential to assess fruit sweetness in intact whole pineapple and DM in mango fruit, respectively, to within 1° Brix and 1% DM, and could be used for the grading of fruit in fruit packing sheds.
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Shankar, Kathiresan, Sachin Kumar, Ashit Kumar Dutta, Ahmed Alkhayyat, Anwar Ja’afar Mohamad Jawad, Ali Hashim Abbas, and Yousif K. Yousif. "An Automated Hyperparameter Tuning Recurrent Neural Network Model for Fruit Classification." Mathematics 10, no. 13 (July 5, 2022): 2358. http://dx.doi.org/10.3390/math10132358.
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Automated fruit classification is a stimulating problem in the fruit growing and retail industrial chain as it assists fruit growers and supermarket owners to recognize variety of fruits and the status of the container or stock to increase business profit and production efficacy. As a result, intelligent systems using machine learning and computer vision approaches were explored for ripeness grading, fruit defect categorization, and identification over the last few years. Recently, deep learning (DL) methods for classifying fruits led to promising performance that effectively extracts the feature and carries out an end-to-end image classification. This paper introduces an Automated Fruit Classification using Hyperparameter Optimized Deep Transfer Learning (AFC-HPODTL) model. The presented AFC-HPODTL model employs contrast enhancement as a pre-processing step which helps to enhance the quality of images. For feature extraction, the Adam optimizer with deep transfer learning-based DenseNet169 model is used in which the Adam optimizer fine-tunes the initial values of the DenseNet169 model. Moreover, a recurrent neural network (RNN) model is utilized for the identification and classification of fruits. At last, the Aquila optimization algorithm (AOA) is exploited for optimal hyperparameter tuning of the RNN model in such a way that the classification performance gets improved. The design of Adam optimizer and AOA-based hyperparameter optimizers for DenseNet and RNN models show the novelty of the work. The performance validation of the presented AFC-HPODTL model is carried out utilizing a benchmark dataset and the outcomes report the promising performance over its recent state-of-the-art approaches.
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Fang, Qiang. "Design of Fruit Sorting Machine of Weighing Type Based on Solid Works." Applied Mechanics and Materials 602-605 (August 2014): 723–26. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.723.
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Fruit sorting machine of weighing type was used most widely in fruit grading market, which study on its structure adopting traditional 2D plan method is too abstract to modify hardly. Therefore a new weighing type of fruit sorting machine was designed using Solid Works, through which the design is for stents, tray institutions, transmission system and sprocket shaft of the sorting machine was realized, and 3D model for the motor reducer and bearing socket was built so that the overall assembly for the fruit sorting machine was achieved. Afterwards,3D modeling, virtual assembly as well as compute clash and primary FEM analysis were carried out for parts of machine. The application results show that Solid works is beneficial to new agricultural machinery design, which shall shorten the development cycle and improve the design quality.
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Huynh, Quoc-Khanh, Chi-Ngon Nguyen, Hong-Phuc Vo-Nguyen, Phuong Lan Tran-Nguyen, Phan-Hung Le, Dang-Khanh-Linh Le, and Van-Cuong Nguyen. "Crack Identification on the Fresh Chilli (Capsicum) Fruit Destemmed System." Journal of Sensors 2021 (February 2, 2021): 1–10. http://dx.doi.org/10.1155/2021/8838247.
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Destemming fresh chilli fruit (Capsicum) in large productivity is necessary, especially in the Mekong Delta region. Several studies have been done to solve this problem with high applicability, but a certain percentage of the output consisted of cracked fruits, thus reducing the quality of the system. The manual sorting results in high costs and low quality, so it is necessary that automatic grading is performed after destemming. This research focused on developing a method to identify and classify cracked chilli fruits caused by the destemming process. The convolution neural network (CNN) model was built and trained to identify cracks; then, appropriate control signals were sent to the actuator for classification. Image processing operations are supported by the OpenCV library, while the TensorFlow data structure is used as a database and the Keras application programming interface supports the construction and training of neural network models. Experiments were carried out in both the static and working conditions, which, respectively, achieved an accurate identification rate of 97 and 95.3%. In addition, a success rate of 93% was found even when the chilli body is wrinkled due to drying after storage time at 120 hours. Practical results demonstrate that the reliability of the model was useful and acceptable.
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Oliveira, Francisco A., Sergio N. Duarte, José F. Medeiros, Carlos JGS Lima, Mychelle KT Oliveira, and Ricardo CP Silva. "Improving sweet pepper yield and quality by means of fertigation management." Horticultura Brasileira 35, no. 2 (April 2017): 235–41. http://dx.doi.org/10.1590/s0102-053620170213.
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ABSTRACT Fertigation can be managed using fixed fertilizer doses or by assessing the ionic concentration of soil solution throughout the growing period. This work studied how different fertigation management systems affected sweet pepper yield and quality. The experiment was carried out in greenhouse, in pots. Fertigation was managed according to the crop uptake rate (M1) or by monitoring either the electrical conductivity (M2) or the N and K concentration (M3) in the soil solution. Fertigation management was combined with six N and K doses (0, 50, 100, 150, 200 and 300% of the recommended dose for sweet pepper in the region), in complete blocks at random, with four replications, and treatments in 3x6 factorial. Dry matter accumulation, yield and fruit grading were evaluated. Fertigation managed by means of monitoring the soil solution improved the vegetative growth in up to 25% and increased yield in up to 20% when compared to management according to the uptake rate. Highest fruit yields in M1, M2 and M3 were achieved with N and K levels corresponding to 127.6% (1.33 kg/plant), 222.5% (1.60 kg/plant) and 215% (1.48 kg/plant), respectively. N and K can be supplied successfully to sweet peppers using electrical conductivity or concentration of ions in the soil solution to manage fertigation. These management systems resulted in high quality fruits and up to 47% increase in N and K use efficiency. N and K concentration equivalent to 200% of the recommended for growing sweet pepper in hydroponics should be taken as reference.
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Zahra, Aryanis Mutia, Tadashi Chosa, and Seishu Tojo. "Fruit Quality Evaluation in The Maturation Process of Blueberries Using Image Processing." Indonesian Food and Nutrition Progress 18, no. 2 (April 14, 2022): 41. http://dx.doi.org/10.22146/ifnp.63897.
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Blueberries' quality does not change uniformly during ripeness. Blueberries should be harvested fully ripened at the post-climacteric stage with an excellent indicator including consistent color, taste, and ease of removal from plant as excellent indicators. Therefore, the blueberries are not harvested until it has the desired blue color. The reliance on human perception on the fruit's taste and appearance might cause inconsistency and inaccurate judgment of the fruit maturation. This study aimed to develop an image processing algorithm capable of classifying blueberry maturity stages. The Bluecrop Northern highbush blueberry was harvested at five different stages of maturity based on visual grading of the fruit color (green, green-red, red, red-blue, and blue) from various fruit positions on the tree. Image processing with discriminant analysis accurately classified maturity stages at 98.3% accuracy. The image quality attributes of blueberries changed significantly at different maturity stages. Overall, most image quality attributes correlated strongly with well-performed blueberry physicochemical properties. This study showed that image processing during the blueberry maturation process could be a reliable and comprehensible method for estimating changes in color, shape, weight, and ultimately changes in specific physicochemical properties. This study also provided a practical evaluation of the maturity stages and physicochemical properties, which were predicted using image processing.
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Mo, Xin Ping, and Zhi Guo Pan. "Study and Application of Machine Vision Technique for Quality Detection of Agricultural Products." Advanced Materials Research 1061-1062 (December 2014): 999–1002. http://dx.doi.org/10.4028/www.scientific.net/amr.1061-1062.999.
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The study and application of machine vision technique for the detection of agricultural products, which includes the quality detection and grading of fruits, dried fruits, vegetables, are discussed. It summarizes the existing problems of detection and grading of agricultural products based on machine vision, and analyzes the methods of solving problems through establishing a national collaborative innovation center.
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Campbell, Craig A. "HANDLING OF FLORIDA-GROWN AND IMPORTED TROPICAL FRUITS AND VEGETABLES." HortScience 27, no. 6 (June 1992): 568a—568. http://dx.doi.org/10.21273/hortsci.27.6.568a.
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The tropical fruit and vegetable industry in South Florida is thriving, with imports of mangos, papayas, and tropical vegetables becoming a major area of expansion. An increasingly aware U.S. public has created a stronger demand for both Florida-grown and imported tropical commodities whose retail quality has increased due to improved handling and transportation practices. Systems for product temperature management, washing, grading, coating, and packaging are being modified to accommodate the conditions present in South Florida, Central and South America, and the Caribbean. The recent widespread approval of hot-water quarantine treatment of mangos has facilitated international trading and allowed U.S. fruit companies to maintain nearly uninterrupted supplies.
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Hassell, Richard L., Jonathan R. Schultheis, Wilfred (Bill) R. Jester, Stephen M. Olson, Donald N. Maynard, and Gilbert A. Miller. "Yield and Quality of Triploid Miniwatermelon Cultivars and Experimental Hybrids in Diverse Environments in the Southeastern United States." HortTechnology 17, no. 4 (January 2007): 608–17. http://dx.doi.org/10.21273/horttech.17.4.608.
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The goal of this study was to evaluate miniwatermelon (Citrullus lanatus) cultivars/experimental hybrids (cultigens) for yield, quality, and adaptability in various growing environments. Eighteen cultigens were evaluated in field locations at southern Florida (Bradenton), northern Florida (Quincy), central South Carolina (Blackville), coastal South Carolina (Charleston), and eastern North Carolina (Kinston). Fruit at each site were harvested when watermelons in several plots were at market maturity. Fruit were categorized as marketable if they weighed between 3.0 and 9.0 lb. Fruit were categorized by size as follows: ≤3.0 lb (cull), 3.1–5.0 lb, 5.1–7.0 lb, 7.1–9.0 lb, and ≥9.1 lb (cull). Fruit were graded according to U.S. Department of Agriculture (USDA) grading standards for all watermelon fruit. We found that eight cultigens (Meilhart, Petite Perfection, Precious Petite, Little Deuce Coupe, RWT 8162, Master, Bibo, and Vanessa) were consistently among the top yielding and four cultigens (HA 5138, HA 5117, Petite Treat, and Valdoria) were consistently among the lowest yielding. These had a consistent yield response regardless of location. Within the small marketable melon category (3.1–5.0 lb), ‘Bibo’, ‘Precious Petite’, and RWT 8162 produced a uniform fruit over the five locations. Within the medium marketable melon category (5.1–7.0 lb) ‘Meilhart’, ‘Little Deuce Coupe’, HA 5109, ‘Xite’, ‘Mohican’, SR 8101, and ‘Vanessa’ produced uniform fruit size over the five locations. HA 5117, HA 5109, ‘Extazy’, ‘Mohican’, ‘Petite Treat’, and ‘Valdoria’ produced more fruit in the larger category. Those cultigens that produced melons that were consistently >9.0 lb were HA 5138, HA 5117, Bobbie, and Valdoria. The larger USDA marketable class (7.1–9.0 lb) was considered too large to be in the miniwatermelon market. We found five cultigens that provided consistently high soluble solids readings at each location: Master, RWT 8162, Betsy, Bobbie, and Bibo. We sampled only five fruit at each location for internal quality, and found dark seeds in all of the cultigens in at least one of the locations. Rind thickness and fruit shape did not appear to be influenced by test site location.