Modelling Techniques to Improve the Quality of Food Using Artificial Intelligence

Sahni, Varsha; Srivastava, Sandeep; Khan, Rijwan

doi:10.1155/2021/2140010

Cited by 59 publications

(23 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…IoT in agriculture and allied focus is automating all elements of farming and farming methods to make the process much more efficient. Traditional conservation farming systems (such as cattle identification) are not entirely automated and have several disadvantages such as increased human involvement, labour costs, electricity usage, and so on [6,7].…”

Section: Introductionmentioning

confidence: 99%

Smart Farming System Based on Intelligent Internet of Things and Predictive Analytics

Ferehan

Haqiq

Ahmad

2022

Journal of Food Quality

View full text Add to dashboard Cite

The Internet of Things (IoT) makes it conceivable to communicate among distinctive things. The use of IoT in the farming industry is critical for increasing utility. Smart agricultural practices may boost crop yield while also creating more output with the same amount of input. The majority of farmers, however, are still unaware of the most recent technologies and procedures. In this study, a revolutionary wireless mobile robot based on the Internet of Things (IoT) is created and installed to perform a variety of outdoor tasks. The benefits of this work include more accurate and efficient data, as well as a reduction in manpower. This research has applications in agriculture, arrival, and water division. Keen agrarian frameworks have been built up in different parts of the world utilising the Internet of Things (IoT) and remote sensor systems. One of the branches that springs to intellect in this respect is exactness cultivating. Numerous analysts have made checking and robotization frameworks for different cultivating capacities. Information collection and transmission between IoT gadgets set in ranches will be basic utilising WSN. The Kalman Filter (KF) is used with expectation investigation within the proposed method to get high-quality information free of commotion and exchange it with cluster-based WSNs. The quality of information utilised for examination is progressed as a result of this strategy, and information transport overhead within the wireless sensor network application is decreased. A decision tree is used for forecast analytics decision making for trim surrender expectation, trim classification, soil classification, climate expectation, and trim malady expectation. IoT components integrated with IoT cloud are coordinates in proposed framework to supply keen arrangement for edit development observing to clients.

show abstract

Section: Introductionmentioning

confidence: 99%

Smart Farming System Based on Intelligent Internet of Things and Predictive Analytics

Ferehan

Haqiq

Ahmad

2022

Journal of Food Quality

View full text Add to dashboard Cite

show abstract

“…The regulation of organic acidis concurrently achieved by activation or inactivation of single genes like GTR1, GTR2, LIP5, LSM1, PHO85, PLM2, RTG1, RTG2, and UBP3 genes [43]. Winerelated yeast, Candida zemplinina, and Saccharomyces cerevisiae produce organic acidic (malic, fumaric and succinic acid) at a level comparable to the production of acid by Saccharomyces species [44][45][46]. Yeast strains have different patterns of consumption and production of organic acids and organic acid management during fermentation [47], which contributes significantly to the perception of wine quality by consumers [48].…”

Section: Reviewmentioning

confidence: 99%

Technological Characteristics of Wild Non-Saccharomyces Sourced from Banana Fruit and Honey

Nnodim

Odu

Ogbonna

et al. 2021

MRJI

View full text Add to dashboard Cite

This study is aimed at evaluating the technological characteristics of wild non-Saccharomyces sourced from banana fruit and wild honey. The isolation of yeasts was done according to standard microbiological procedures. Technological traits screened for are as follows: fermentation ability, alcohol production, flocculation ability, organic acid production, and hydrogen sulphide production. Five yeast isolates were identified as B10 (Candida tropicalis), B7 (Candida tropicalis), H4 (Candida tropicalis), H7 (Clavisporalusitaniae), and CY (Candida tropicalis), which are sugar fermenters. The percentage of alcohol produced from each sugar fermented by the yeast isolates are as follows: sucrose - B7(11.50%) > H7(8.62%) > CY (7.80%) > H4(4.88%) > B10 (4.11%); Glucose - B7(9.82%) > CY (6.28%) > B10(4.56%) > H7(4.03%) > H4(2.19%) and Fructose - H7(13.11%) > CY (9.40%) > B10(7.03%) > H4(4.41%) > B7(3.70%). Yeast isolate CY demonstrated high flocculation of 28.55 and 44.75 (%) at 5 and 15 (minutes). The organic acid produced by the yeast isolates B10, B7, CY, H4 and H7 are as follows 1.90±0.41, 3.10±0.41, 1.25±0.07, 3.90±0.41 and 2.40±0.41 (AU) respectively and Yeast isolates B7, CY, H4, and H7 produced low hydrogen sulphite concentration. Wild non-Saccharomyces could be the hope of the wine microbiologist to ease the challenges in the wine industry, as they competed flavourably with the commercial wine yeast.

show abstract

“…Agriculture contributes for around 15% of GDP in developing nations, such as India, and is a significant source of revenue for the Indian economy. Sierra Leone (60% of GDP), the Central African Republic (39.6%), and so on are among the few countries whose GDP is substantially based on agriculture [3,4]. In the early ninety century, the farmers were facing lots of difficulty to maintain the crops and vegetable quality before selling to the market.…”

Section: Introductionmentioning

confidence: 99%

Smart Logistic System for Enhancing the Farmer-Customer Corridor in Smart Agriculture Sector Using Artificial Intelligence

Ramírez-Asís

Bhanot

Jagota³

et al. 2022

Journal of Food Quality

View full text Add to dashboard Cite

In an agriculture sector, the quality of several raw crops depends upon the time factor. After harvesting, it is necessary to bring the crops either to cold storage or directly to customer though wholesale dealers. Keeping crops in cold storage decrease the nutrition value and also increase the overall cost of the crop leading to price hike. So, it will be best if the crops are put in the market as soon as possible. This can only be possible if the logistic system is updated and can handle the real-time requirement of agriculture product transport. This study investigates and highlights the possible IoT-based logistic support using artificial intelligence for the farmers such that a fast corridor is created between farmers’ lands to end-user customers. This will benefit the farmers in two-fold; first, it will increase the revenue of farmers by decreasing the time span avoiding cold storage fees, and second, it will maintain the quality of crops.

show abstract

Modelling Techniques to Improve the Quality of Food Using Artificial Intelligence

Cited by 59 publications

References 48 publications

Smart Farming System Based on Intelligent Internet of Things and Predictive Analytics

Smart Farming System Based on Intelligent Internet of Things and Predictive Analytics

Technological Characteristics of Wild Non-Saccharomyces Sourced from Banana Fruit and Honey

Smart Logistic System for Enhancing the Farmer-Customer Corridor in Smart Agriculture Sector Using Artificial Intelligence

Contact Info

Product

Resources

About