Facility tomato picking system based on shelf life prediction and fruit maturity discrimination
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In order to ensure the best taste and maturity of the picked tomatoes during the market, this paper designed a facility tomato picking system based on shelf life prediction and fruit maturity discrimination. Use the deep learning method to predict the shelf life of tomatoes and distinguish the ripeness of tomato fruits, and calculate the best picking time of tomatoes through the shelf life and fruit ripeness. The use of this system avoids the uncertainty of empirical judgment of picking time, improves economic benefits, and is of great significance to the development of intelligent fruit and vegetable picking.Keywords:
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The purpose of this project is to detect the ripeness and quality of the watermelon particularly for red watermelon. The ripeness of the watermelon will be evaluated by using near-infrared spectroscopy sensor (NRIS). The color wavelength will classify the ripeness of the watermelon. An infrared light will be used to get the appropriate wavelength from the watermelon either from the rind or inner of it and the signal received will be analyzed. An appropriate algorithm is used to extract the information of the inner of the watermelon. A microcontroller namely Programmable Interface Controller (PIC) will be used to execute the algorithm and the result will be displayed on Liquid Crystal Display (LCD). Based on the result obtain from the device, the data is computed by using Statistical Package for the Social Sciences (SPSS). This approach is vital to verify the relationship between unripe and ripeness of red watermelon. The objective of this project is to produce an efficient system to detect the ripeness of the watermelon.
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Grape ripeness is an elusive concept for many people and sometimes an elusive achievement for vineyards. Much of the difficulty with discussions of grape ripeness is that there is often an implied standard, but in reality, ripeness is an entirely subjective judgment. So, there are really two issues to address: 1) how do we define grape ripeness and 2) how do we measure ripeness parameters to assist our harvest decisions.
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India is worldwide well known for exportingfruits, having immense importance in the world. Globalfood security is necessary for not only durableproduction of fruits but also for remarkable reduction inpre and post- harvest waste. Harvesting fruits anddetecting ripeness of fruits by human is an expensive,laborious and time consuming task. For this reason,there is need for an automated ripeness estimationsystem in the last decade. Fruit ripeness estimation ismajor task that influence its quality and later itsmarketing. Researchers have started targeting towardsfor the study of ripeness estimation using methods inimage processing and machine learning to automaticclassification of ripeness of fruit accurately, quickly andnon-destructively. Traditional methods for fruit ripenessestimation considered fruits such as orange, apple,tomato, banana, papaya and etc. which is single fruit. Bytaking into account increasing productivity of grapesand bunch of berries in grapes need to focus onestimation of ripeness of grapes fruit. We have reviewedvarious studies in this domain and believe this is aprimary effort in summarizing the highlights ofresearches done. This will give direction for fellowresearchers.
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In this paper, some physical and mechanical properties of banana fruits at different level of ripeness were investigated. Relation between various stages of ripeness and these properties were determined and correlation coefficients were calculated. The color of the fruit skin was measured as L*, a* and b* in CIELAB system. The mechanical properties were extracted from plotted force-deformation curve. A significant difference at 5% level was found between the level of ripeness and these properties. Duncan's multiple range test was conducted and results were reported. Results showed that changes in L*, b* and C was similar, also variation of color index (CI) was similar to a*. The firmness, rupture energy and hardness decreased as banana fruit ripened. All measured physico- mechanical properties of banana fruit except deformation had High correlation with stage of ripeness. Result of deformation analysis showed no significant difference at various stages of ripeness. The correlation between deformation and stage of ripeness was obtained as 0.2. (Mahmoud Soltani, Reza Alimardani, Mahmoud Omid. Changes in physico-mechanical properties of banana fruit during ripening treatment. Journal of American Science 2011;7(5):14-19). (ISSN: 1545-1003). http://www.americanscience.org.
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In order to measure the ripeness of an apple without cutting, an equipment with a vibrator and a pick-up was devised.Ripeness of a Starking apple examined by this equipment was compared with that of sensory test to find the result that the maximum value of amplitude possible to be employed as an indicator of ripeness.
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This paper aims to study appropriate ripeness of cultivated banana and time period for vacuum fry. Sliced bananas of thickness 1-2 mm were fried at temperature 130 ๐C under vacuum condition. The samplings were done at 3 level of ripeness; 1) raw banana, 2) the first day of ripeness, 3) the second day of ripeness. In addition 5 levels of fried period were set; 6, 8, 10, 12, 14 min. The raw banana fried for 12 min shown the best result. The products have a crisp texture, appropriate hardness and toughness, standard moisture and color with the most acceptance by consumers.
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Fruit ripeness is an important thing in agriculture because it determines the fruit's quality. Determining the ripeness of the fruit that was done manually poses several weaknesses, such as takes a relatively long time, requires a lot of labor, and can cause inconsistencies. The agricultural sector is one of the essential sectors of the economy in Indonesia. However, sometimes the process of determining fruit ripeness is still done by using the manual method. The development of computer vision and machine learning technologies can be used to classify fruit ripeness automatically. This study applies the Convolutional Neural Network to classify the ripeness of the banana. The banana's ripeness is divided into four classes: unripe/green, yellowish-green, mid-ripen, and overripe. Two pre-trained models are used, which are MobileNet V2 and NASNetMobile. The experiment was conducted using Google Colab and several libraries such as OpenCV, Tensorflow, and scikit-learn. The result shows that MobileNet V2 achieves higher accuracy and faster execution time than the NASNetMobile. The highest accuracy achieved is 96.18%.
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Abstract The influence of the stage of ripening of the fruit of cultivars of plantain (French Sombre) and some cooking bananas (Dwarf Kalapua and Bluggoe) on the sensory and physico‐chemical characteristics of processed products was evaluated. Chips made from these cultivars at corresponding stages of ripeness had water contents less than 20 g kg −1 for fruits at stages 1 and 3 and less than 60 g kg −1 for those at stages 4 and 5. There was no significant difference ( p > 0.05) between the ash contents of chips at different stages of ripeness for all the cultivars. Protein contents increased with increasing ripeness for all the cultivars; the fat contents decreased with increasing ripeness and varied from one cultivar to another. The available food energy was more than 4840 kcal kg −1 of chips for all the cultivars at all stages of ripeness. The best chips were obtained from fruits at stages 1 and 3. Flours obtained from the fruits of these cultivars at different stages of ripeness had water contents lower than 60 g kg −1 . The fat, ash and protein contents were low, while the carbohydrate contents were high. For all the cultivars the yields of chips and flour were higher for the plantain cultivar (French Sombre) than for the cooking banana cultivars, irrespective of the stage of ripeness of the fruits. Cakes made from the different flours had good nutritional quality. The cakes and chips submitted for sensory evaluation were all accepted by consumers, although to differing extents. Fruits at stages 1 and 3 of ripeness which presented fewer problems during drying were the most suitable for the manufacture of flour for making cakes. Copyright © 2004 Society of Chemical Industry
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