Detection of Rotten Fruits and Vegetables Using Deep Learning

2021 ◽  
pp. 31-49
Author(s):  
Susovan Jana ◽  
Ranjan Parekh ◽  
Bijan Sarkar
Keyword(s):  
Deep Learning ◽  
Fruits And Vegetables

scholarly journals Classification of fruits and vegetables using ResNet model.

2021 ◽  
Author(s):  
P. Sukhetha ◽  
N. Hemalatha ◽  
Raji Sukumar
Keyword(s):  
Deep Learning ◽  
Fruits And Vegetables ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Agricultural Field ◽  
K Nearest Neighbor ◽  
New Techniques ◽  
Learning Techniques ◽  
A Current

Abstract Agriculture is one of the important parts of Indian economy. Agricultural field has more contribution towards growth and stability of the nation. Therefore, a current technologies and innovations can help in order to experiment new techniques and methods in the agricultural field. At Present Artificial Intelligence (AI) is one of the main, effective, and widely used technology. Especially, Deep Learning (DL) has numerous functions due to its capability to learn robust interpretations from images. Convolutional Neural Networks (CNN) is the major Deep Learning architecture for image classification. This paper is mainly focus on the deep learning techniques to classify Fruits and Vegetables, the model creation and implementation to identify Fruits and Vegetables on the fruit360 dataset. The models created are Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Tree (DT), ResNet Pretrained Model, Convolutional Neural Network (CNN), Multilayer Perceptron (MLP). Among the different models ResNet pretrained Model performed the best with an accuracy of 95.83%.

scholarly journals Agricultural harvesting using integrated robot system

2022 ◽  
Vol 25 (1) ◽  
pp. 152
Author(s):  
Vikram Raja ◽  
Bindu Bhaskaran ◽  
Koushik Karan Geetha Nagaraj ◽  
Jai Gowtham Sampathkumar ◽  
Shri Ram Senthilkumar
Keyword(s):  
Deep Learning ◽  
Fruits And Vegetables ◽  
Cost Effective ◽  
Robotic Manipulators ◽  
Manual Labor ◽  
Improve Quality ◽  
Robot System ◽  
Image Detection ◽  
Viable Solution ◽  
Manual Methods

In today's competitive world, robot designs are developed to simplify and improve quality wherever necessary. The rise in technology and modernization has led people from the unskilled sector to shift to the skilled sector. The agricultural sector's solution for harvesting fruits and vegetables is manual labor and a few other agro bots that are expensive and have various limitations when it comes to harvesting. Although robots present may achieve harvesting, the affordability of such designs may not be possible by small and medium-scale producers. The integrated robot system is designed to solve this problem, and when compared with the existing manual methods, this seems to be the most cost-effective, efficient, and viable solution. The robot uses deep learning for image detection, and the object is acquired using robotic manipulators. The robot uses a Cartesian and articulated configuration to perform the picking action. In the end, the robot is operated where carrots and cantaloupes were harvested. The data of the harvested crops are used to arrive at the conclusion of the robot's accuracy.

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.
Keyword(s):  
Deep Learning ◽  
Fruits And Vegetables ◽  
Learning Strategy ◽  
Confusion Matrix ◽  
Climacteric Fruit ◽  
Dragon Fruit ◽  
Human Labor ◽  
Progress Rate ◽  
Vegetables And Fruits ◽  
Region Of Convergence

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.

Flavonoids in adipose tissue inflammation and atherosclerosis: one arrow, two targets

2020 ◽  
Vol 134 (12) ◽  
pp. 1403-1432 ◽  
Author(s):  
Manal Muin Fardoun ◽  
Dina Maaliki ◽  
Nabil Halabi ◽  
Rabah Iratni ◽  
Alessandra Bitto ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Fruits And Vegetables ◽  
Metabolic Diseases ◽  
Therapeutic Agents ◽  
Adipose Tissue Inflammation ◽  
Cellular Mechanisms ◽  
Tissue Inflammation ◽  
Cholesterol Levels ◽  
Naturally Occurring ◽  
Pro Inflammatory Cytokines

Abstract Flavonoids are polyphenolic compounds naturally occurring in fruits and vegetables, in addition to beverages such as tea and coffee. Flavonoids are emerging as potent therapeutic agents for cardiovascular as well as metabolic diseases. Several studies corroborated an inverse relationship between flavonoid consumption and cardiovascular disease (CVD) or adipose tissue inflammation (ATI). Flavonoids exert their anti-atherogenic effects by increasing nitric oxide (NO), reducing reactive oxygen species (ROS), and decreasing pro-inflammatory cytokines. In addition, flavonoids alleviate ATI by decreasing triglyceride and cholesterol levels, as well as by attenuating inflammatory mediators. Furthermore, flavonoids inhibit synthesis of fatty acids and promote their oxidation. In this review, we discuss the effect of the main classes of flavonoids, namely flavones, flavonols, flavanols, flavanones, anthocyanins, and isoflavones, on atherosclerosis and ATI. In addition, we dissect the underlying molecular and cellular mechanisms of action for these flavonoids. We conclude by supporting the potential benefit for flavonoids in the management or treatment of CVD; yet, we call for more robust clinical studies for safety and pharmacokinetic values.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Homocysteine Levels Show Significant Differences among Mediterranean Dietary Quality Index Variables Compared to Folate and Vitamin B12 Status in Women

2015 ◽  
Vol 85 (3-4) ◽  
pp. 202-210 ◽  
Author(s):  
Ivona Višekruna ◽  
Ivana Rumbak ◽  
Ivana Rumora Samarin ◽  
Irena Keser ◽  
Jasmina Ranilović
Keyword(s):  
Mediterranean Diet ◽  
Diet Quality ◽  
Quality Index ◽  
Fruits And Vegetables ◽  
Reproductive Age ◽  
Dietary Quality ◽  
Vitamin B ◽  
Cross Sectional ◽  
Poor Diet ◽  
The Mediterranean

Abstract. Results of epidemiologic studies and clinical trials have shown that subjects following the Mediterranean diet had lower inflammatory markers such as homocysteine (Hcy). Therefore, the aim of this cross-sectional study was to assess female diet quality with the Mediterranean diet quality index (MDQI) and to determine the correlation between MDQI, homocysteine, folate and vitamin B12 levels in the blood. The study participants were 237 apparently healthy women (96 of reproductive age and 141 postmenopausal) between 25 and 93 years. For each participant, 24-hour dietary recalls for 3 days were collected, MDQI was calculated, and plasma Hcy, serum and erythrocyte folate and vitamin B12 levels were analysed. Total MDQI ranged from 8 to 10 points, which represented a medium-poor diet for the subjects. The strength of correlation using biomarkers, regardless of group type, age, gender and other measured parameters, was ranked from best (0.11) to worst (0.52) for olive oil, fish, fruits and vegetables, grains, and meat, in this order. Hcy levels showed the best response among all markers across all groups and food types. Our study shows significant differences between variables of the MDQI and Hcy levels compared to levels of folate and vitamin B12 in participants with medium-poor diet quality, as evaluated according to MDQI scores.

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