Modelling Techniques to Improve the Quality of Food Using Artificial Intelligence

Artificial intelligence (AI), or AI/machine vision, is assuming an overwhelming part in the realm of food handling and quality affirmation. As indicated by Mordor Intelligence, AI in the food and refreshments market is required to enlist a CAGR of 28.64%, during the conjecture time frame 2018–2023. Artificial intelligence makes it workable for PCs to gain as a matter of fact, investigate information from the two data sources and yields, and perform most human assignments with an improved level of accuracy and proficiency. Here is a concise gander at how AI is expanding sanitation and quality activities. This exploration has along these lines tried to furnish policymakers with a way to assess new and existing strategies, while likewise offering a reasonable premise through which food chains orders can be made stronger through the thought of the executive’s practices and strategy choices. This survey centers on the AI applications according to four mainstays of food security that is food accessibility, food availability, food use, and strength.

[1]  Understanding Smallholder Farmers’ Intention to Adopt Agricultural Apps: The Role of Mastery Approach and Innovation Hubs in Mexico , 2021 .

[2]  J.H.J. Spiertz,et al.  Development and validation of SUCROS-Cotton: a potential crop growth simulation model for cotton , 2008 .

[3]  Varsha Turkar,et al.  Cucumber disease detection using artificial neural network , 2016, 2016 International Conference on Inventive Computation Technologies (ICICT).

[4]  A. Ogundeji,et al.  Adoption of soil and water conservation technology and its effect on the productivity of smallholder rice farmers in Southwest Nigeria , 2021, Heliyon.

[5]  Laécio C. Barros,et al.  An approach via fuzzy systems for dynamics and control of the soybean aphid , 2015, IFSA-EUSFLAT.

[6]  Donald W. Boyd,et al.  Prototyping an expert system for diagnosis of potato diseases , 1994 .

[7]  Mercedes Valdés-Vela,et al.  Soft computing applied to stem water potential estimation: A fuzzy rule based approach , 2015, Comput. Electron. Agric..

[8]  Yan Liu,et al.  A fuzzy decision tool to evaluate the sustainable performance of suppliers in an agrifood value chain , 2019, Comput. Ind. Eng..

[9]  Carolyn M. Beans Inner Workings: Crop researchers harness artificial intelligence to breed crops for the changing climate , 2020, Proceedings of the National Academy of Sciences.

[10]  M. M. Rahman,et al.  MODELLING OF JUTE PRODUCTION USING ARTIFICIAL NEURAL NETWORKS , 2010 .

[11]  Raj Kamal,et al.  A hybrid ensemble for classification in multiclass datasets: An application to oilseed disease dataset , 2016, Comput. Electron. Agric..

[12]  Jerry Shannon,et al.  When are food deserts? Integrating time into research on food accessibility. , 2014, Health & place.

[13]  Rahman Khatibi,et al.  Experimental studies on scour of supercritical flow jets in upstream of screens and modelling scouring dimensions using artificial intelligence to combine multiple models (AIMM) , 2019 .

[14]  Jorge Chanona-Pérez,et al.  Early detection of mechanical damage in mango using NIR hyperspectral images and machine learning , 2014 .

[15]  B. Damayanthi,et al.  Factors Influencing the Youths’ Interest in Agricultural Entrepreneurship in Sri Lanka , 2019, Developing Country Studies.

[16]  Dariush Zare,et al.  An integrated energy and quality approach to optimization of green peas drying in a hot air infrared-assisted vibratory bed dryer , 2015 .

[17]  Pratibha Phaiju Towards Food Security through Artificial Neural Network , 2019 .

[18]  Philip Zanotti,et al.  Drive Through Robotics: Robotic Automation for Last Mile Distribution of Food and Essentials During Pandemics , 2020, IEEE Access.

[19]  Mohammad Mahdi Paydar,et al.  A bi-objective optimization for citrus closed-loop supply chain using Pareto-based algorithms , 2018, Appl. Soft Comput..

[20]  Sanjay Sharma,et al.  Key indicators of rice production and consumption, correlation between them and supply-demand prediction , 2015 .

[21]  Darko Stefanovic,et al.  Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification , 2016, Comput. Intell. Neurosci..

[22]  D. Wunsch,et al.  Examining plant uptake and translocation of emerging contaminants using machine learning: Implications to food security. , 2019, The Science of the total environment.

[23]  Rudolf Kruse,et al.  Data Mining with Neural Networks for Wheat Yield Prediction , 2008, ICDM.

[24]  Zahir Irani,et al.  Artificial intelligence and food security: swarm intelligence of AgriTech drones for smart AgriFood operations , 2021, Production Planning & Control.

[25]  Xuhui Zhao,et al.  Multispectral Detection of Citrus Canker Using Hyperspectral Band Selection , 2011 .

[26]  Momoh Jimoh Eyiomika Salami,et al.  Fuzzy logic based intelligent temperature controller for cassava post-harvest storage system , 2015 .

[27]  A. Adedeji,et al.  Hyperspectral imaging for detection of codling moth infestation in GoldRush apples , 2017 .

[28]  E. Russell The United Nations Food and Agriculture Organization , 1955, Nature.

[29]  Suhas Athani,et al.  Soil moisture monitoring using IoT enabled arduino sensors with neural networks for improving soil management for farmers and predict seasonal rainfall for planning future harvest in North Karnataka — India , 2017, 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC).

[30]  S. Prasher,et al.  Pork quality and marbling level assessment using a hyperspectral imaging system , 2007 .

[31]  P. F. Trugilho,et al.  Characterization of residues from plant biomass for use in energy generation , 2011 .

[32]  Sahar A. Mokhtar,et al.  Weeds identification using Evolutionary Artificial Intelligence Algorithm , 2014, J. Comput. Sci..

[33]  H. Herren,et al.  Farm Sustainability Assessment using the IDEA Method. From the concept of farm sustainability to case studies on French farms , 2006 .

[34]  Paolo Valdez,et al.  Apple Defect Detection Using Deep Learning Based Object Detection For Better Post Harvest Handling , 2020, ArXiv.

[35]  Abhijeet Singh,et al.  An Expert System for diagnosis of diseases in Rice Plant , 2010 .

[36]  Min Zhang,et al.  Recent developments in smart freezing technology applied to fresh foods , 2017, Critical reviews in food science and nutrition.

[37]  Hanping Mao,et al.  Farmer Cooperatives’ Intention to Adopt Agricultural Information Technology—Mediating Effects of Attitude , 2019, Inf. Syst. Frontiers.

[38]  Uchitha Jayawickrama,et al.  The role of Artificial Intelligence networks in sustainable supply chain finance for food and drink industry , 2021, Int. J. Prod. Res..

[39]  Marta Schuhmacher,et al.  A fuzzy expert system for soil characterization. , 2008, Environment international.

[40]  Klaus Gottschalk,et al.  Improved climate control for potato stores by fuzzy controllers , 2003 .

[41]  Nikola Naumov,et al.  The Impact of Robots, Artificial Intelligence, and Service Automation on Service Quality and Service Experience in Hospitality , 2019, Robots, Artificial Intelligence, and Service Automation in Travel, Tourism and Hospitality.

[42]  Gang Liu,et al.  Design of Control System of Laser Leveling Machine Based on Fussy Control Theory , 2007, CCTA.

[43]  N. Hooker,et al.  Trends in food quality regulation: Implications for processed food trade and foreign direct investment , 1996 .

[44]  Michael Okpara,et al.  Corrosion inhibitive properties and adsorption behaviour of ethanol extract of Piper guinensis as a green corrosion inhibitor for mild steel in H 2 SO 4 , 2008 .

[45]  Vincent Martin,et al.  A cognitive vision approach to early pest detection in greenhouse crops , 2008 .

[46]  Tetsuo Morimoto,et al.  Optimization of heat treatment for fruit during storage using neural networks and genetic algorithms , 1997 .

[47]  J. McCluskey,et al.  Predicting access to healthful food retailers with machine learning☆ , 2020, Food Policy.

[48]  B. Ji,et al.  Artificial neural networks for rice yield prediction in mountainous regions , 2007, The Journal of Agricultural Science.

[49]  Sammy A. Perdomo,et al.  Weed detection in rice fields using aerial images and neural networks , 2016, 2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA).

[50]  Frank Z. Riely,et al.  Food Security Indicators and Framework for Use in the Monitoring and Evaluation of Food Aid Programs , 2000 .

[51]  Yingwei Yan,et al.  Multiple Regression and Artificial Neural Network for the Prediction of Crop Pest Risks , 2015, ISCRAM-med.

[52]  Brandon T. Bestelmeyer,et al.  Scaling Up Agricultural Research With Artificial Intelligence , 2020, IT Professional.

[53]  Ken Thompson,et al.  Corrigendum to: New handbook for standardised measurement of plant functional traits worldwide , 2016 .