scholarly journals Mellowness Detection of Dragon Fruit Using Deep Learning Strategy

2020 ◽  
Vol 2 (1) ◽  
pp. 35-43 ◽  
Author(s):  
Dr. Vijayakumar T. ◽  
Mr. Vinothkanna R.

The agriculture being a main source of income in many developing countries such as India, Indonesia, etc. The economic development of these countries depends on the GDP (Gross Domestic Progress) rate of the agricultural products. However due to miscalculations in the maturity of the fruits and vegetables leads to the wastage of foods. In general many measure were taken to minimize the food spoilage and by tracking the each stage of the vegetables and fruits carefully, but resulted in a hefty human labor, and weariness. Specifically the non-climacteric fruit such as the dragon fruit requires much attention as it is has to be harvested after it is ripened and cannot be ripened after harvesting using the hastening ripening process such as the ethylene, carbide, and CO2 etc. So the paper has put forth the application to identify the mellowness in the dragon fruit using the RESNET 152 a deep learning convolution neural network to identify the dragon fruits mellowness and it’s time to harvest. The model was trained using the python and the tensor flow. The developed structure was trained using the pictures of the dragon fruit in the different stages of its mellowness and was tested using the region of convergence and the confusion matrix with 100 new data. The testing was carried with the different number of epoch ranging from 10 to 500. The results obtained were more accurate compared to the VGG16 /19 in the terms of Accuracy and loss in training and testing.

2020 ◽  
Vol 3 (2) ◽  
pp. 35 ◽  
Author(s):  
I Made Wismadi ◽  
Duman Care Khrisne ◽  
I Made Arsa Suyadnya

This study has a purpose to develop an application to detect the ripeness of the dragon fruit with the deep learning approach using the Smaller VGGNet-like Network method. In this study, the dragon fruit are classified into two classes: ripe or ready for harvest and still raw, by using the Convolutional Neural Network (CNN). The training process utilize the hard packages in python with the backend tensorflow. The model in this research is tested using the confusion matrix and ROC method with the condition that 100 new data are tested. Based on the test conducted, the level of accuracy in classifying the ripeness of the dragon fruit is 91%, and the test using 20 epoch, 50 epoch, 100 epoch, and 500 epoch produced an AUROC value of 0,95.


2014 ◽  
Vol 4 (2) ◽  
pp. 64-68
Author(s):  
Aleksandra Michalska

Abstract Nowadays, thanks to greater awareness of society and development of restorative medicine, more and more attention is paid to preventive care. That is caused by the fact that there is little progress for both sexes in the frequency of healthy behavior: girls fall much worse than boys in terms of frequency of physical activity, they do not eat breakfast either; boys do not maintain a healthy diet and are reluctant to eat vegetables and fruits, they often drink high-calorie sodas and less frequently brush teeth. Though with age some improvements in oral hygiene and certain eating behaviors can be noticed. It has been determined that overweight and obesity is a serious problem, as they can contribute to developmental disorders. In this respect it should be the responsibility of teachers to provide individual physical education (according to medical qualifications), prevent various forms of discrimination and bullying among peers, provide individual counseling and health education, weight control of students. It has been defined that for modern teachers it is a difficult task as students rarely eat fruits and vegetables, do not care about hygiene and frequency of meals, have passive mode of leisure. The acquisition of health during puberty allows functioning smoothly in society. However, despite the continuous work on improving and introduction of new programs of health education classes into schools of Poland, children still suffer from health-related problems. According to epidemiological research most of children in Poland fall on obesity, overweight and accompanying disorders and allergies. Youth is also exposed to accidents and related injuries. The problem is that students do not receive assistance and necessary information.


2018 ◽  
Vol 16 (1) ◽  
pp. 103-117
Author(s):  
Nurul Istiqomah ◽  
Nunung Sri Mulyani ◽  
Izza Mafruhah ◽  
Dewi Ismoyowati

Indonesia as an agricultural country has the potential to compete in the agricultural market in the international market, in line with the existence of the ASEAN / ASEAN Free Trade Area (AFTA) Free Market. Ngawi Regency is a fertile area and is one of the buffer zones of the agricultural sector in East Java. Horticulture commodities are one of the main sources in the agricultural sector, because they have high potential and can contribute to the economy of a region. Horticultural commodities in the form of fruits and vegetables are an important food source to meet the nutritional needs of the community. Agriculture with a focus on horticultural crops in Ngawi Regency was developed with a cluster system based on the level of progress, harvest area and by considering agro-climate to map the superior horticultural commodities. The purpose of this study was to map the conditions of horticultural agriculture and analyze problems in the cluster of horticulture plants in Ngawi Regency. The research method is a mixed method using descriptive analysis, Geographic Information System (GIS), and using the Analysis Hierarchy Process (AHP). The conclusion of this study is that the potential development of horticultural clusters in Ngawi Regency requires structuring and developing the location of base commodities in accordance with the conditions of the agro-ecosystem. The development of existing commodities at these base points will make the commodity superior and support the creation of horticultural cluster centers and the development of existing agribusiness in an area. Development of horticulture base commodities for seasonal vegetables and fruits can be adjusted to the LQ results for each sub-district in Ngawi Regency. The results of the Indepth interview processed using AHP obtained results that in fact there were three main factors in the development of clusters, namely production consisting of four derivative factors namely research and development, superior seeds, fertilizers and anti-pest drugs and then marketing with derivative factors namely product standardization, packaging , traditional markets and modern markets. Then the third factor of the institution consists of training, networking, government support and assistance. 


2020 ◽  
Vol 16 (6) ◽  
pp. 3721-3730 ◽  
Author(s):  
Xiaofeng Yuan ◽  
Jiao Zhou ◽  
Biao Huang ◽  
Yalin Wang ◽  
Chunhua Yang ◽  
...  

Fermentation ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 60
Author(s):  
Vincenzo Michele Sellitto ◽  
Severino Zara ◽  
Fabio Fracchetti ◽  
Vittorio Capozzi ◽  
Tiziana Nardi

From a ‘farm to fork’ perspective, there are several phases in the production chain of fruits and vegetables in which undesired microbial contaminations can attack foodstuff. In managing these diseases, harvest is a crucial point for shifting the intervention criteria. While in preharvest, pest management consists of tailored agricultural practices, in postharvest, the contaminations are treated using specific (bio)technological approaches (physical, chemical, biological). Some issues connect the ‘pre’ and ‘post’, aligning some problems and possible solution. The colonisation of undesired microorganisms in preharvest can affect the postharvest quality, influencing crop production, yield and storage. Postharvest practices can ‘amplify’ the contamination, favouring microbial spread and provoking injures of the product, which can sustain microbial growth. In this context, microbial biocontrol is a biological strategy receiving increasing interest as sustainable innovation. Microbial-based biotools can find application both to control plant diseases and to reduce contaminations on the product, and therefore, can be considered biocontrol solutions in preharvest or in postharvest. Numerous microbial antagonists (fungi, yeasts and bacteria) can be used in the field and during storage, as reported by laboratory and industrial-scale studies. This review aims to examine the main microbial-based tools potentially representing sustainable bioprotective biotechnologies, focusing on the biotools that overtake the boundaries between pre- and postharvest applications protecting quality against microbial decay.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Monika Rusin ◽  
Joanna Domagalska ◽  
Danuta Rogala ◽  
Mehdi Razzaghi ◽  
Iwona Szymala

AbstractChemical contamination of foods pose a significant risk to consumers. A source of this risk is due to the consumption of products contaminated with heavy metals such as cadmium (Cd) and lead (Pb). The aim of the study was to research the levels of Cd and Pb contamination of selected species of vegetables and fruits in the form of fresh, frozen, dried and processed products. The goal was to verify which of these food groups was more contaminated with heavy metals. The study covered 370 samples of fruits and vegetables including apples, pears, grapes, raspberries, strawberries, cranberries, as well as beetroots, celeries, carrots and tomatoes. The content of Cd and Pb was determined by atomic absorption spectrometry. Quantitative results were analyzed using statistical models: analysis of variance, outlier analysis, post-hoc multiple comparison Tukey test. The tests showed that the levels of Cd and Pb concentration in samples of fresh, processed, frozen and dried fruits and vegetables varied substantially. The highest concentrations were recorded in dried products. Several fruit and vegetable samples exceeded the maximum permissible concentrations of Cd and Pb. The contamination of these products could be a significant source of consumer exposure to heavy metals when these products are a part of the diet.


2019 ◽  
Vol 73 (5) ◽  
pp. 565-573 ◽  
Author(s):  
Yun Zhao ◽  
Mahamed Lamine Guindo ◽  
Xing Xu ◽  
Miao Sun ◽  
Jiyu Peng ◽  
...  

In this study, a method based on laser-induced breakdown spectroscopy (LIBS) was developed to detect soil contaminated with Pb. Different levels of Pb were added to soil samples in which tobacco was planted over a period of two to four weeks. Principal component analysis and deep learning with a deep belief network (DBN) were implemented to classify the LIBS data. The robustness of the method was verified through a comparison with the results of a support vector machine and partial least squares discriminant analysis. A confusion matrix of the different algorithms shows that the DBN achieved satisfactory classification performance on all samples of contaminated soil. In terms of classification, the proposed method performed better on samples contaminated for four weeks than on those contaminated for two weeks. The results show that LIBS can be used with deep learning for the detection of heavy metals in soil.


2021 ◽  
pp. 20200172
Author(s):  
Münevver Coruh Kılıc ◽  
Ibrahim Sevki Bayrakdar ◽  
Özer Çelik ◽  
Elif Bilgir ◽  
Kaan Orhan ◽  
...  

Objective: This study evaluated the use of a deep-learning approach for automated detection and numbering of deciduous teeth in children as depicted on panoramic radiographs. Methods and materials: An artificial intelligence (AI) algorithm (CranioCatch, Eskisehir-Turkey) using Faster R-CNN Inception v2 (COCO) models were developed to automatically detect and number deciduous teeth as seen on pediatric panoramic radiographs. The algorithm was trained and tested on a total of 421 panoramic images. System performance was assessed using a confusion matrix. Results: The AI system was successful in detecting and numbering the deciduous teeth of children as depicted on panoramic radiographs. The sensitivity and precision rates were high. The estimated sensitivity, precision, and F1 score were 0.9804, 0.9571, and 0.9686, respectively. Conclusion: Deep-learning-based AI models are a promising tool for the automated charting of panoramic dental radiographs from children. In addition to serving as a time-saving measure and an aid to clinicians, AI plays a valuable role in forensic identification.


Author(s):  
Kapil Telang ◽  
Rahul Jain ◽  
Ajoy Sodani ◽  
Prachi Shaw ◽  
Susmit Kosta

The current study was aimed to find out whether the COVID-19 virus is detectable upon the fruits and vegetables after coming in close contact with a patient suffering from nSARS-CoV-2. We included ten subjects, who tested positive for nSARS-CoV-2 RNA within seven days of the experiment. After explaining the experiment, a tray filled with seasonal vegetables and fruits were placed in front of them. The tray remained within their reach, for next thirty minutes. The subjects were requested to remove their face masks and remain so throughout the task. They were requested to manipulate the food articles the way they liked. Subjects were instructed to cough into their hands and then to manipulate each item at least 5 times, during the experiment. Thereafter, the trays were moved into an open and shaded area with free flow of natural air but no direct sunlight. After 1-hour, swabs were taken from surfaces of items by thoroughly rubbing over each of them. Samples were sent immediately to our RT-PCR lab. The nSARS-CoV-2 RNA was not detected, from the samples collected from the fruit/vegetable, at the end of one hour of the direct exposure to the COVID-19 patients. Our results suggest, even after direct exposure to and significant handling by the COVID-19 patients the nSARS-CoV-2 RNA remains undetected after one hour of storage in open. The fruits and vegetables, in real-life situations, are unlikely to act as a fomite and play any significant role in the spread of this disease.


Sign in / Sign up

Export Citation Format

Share Document