Artificial Intelligence: Implications for the Agri-Food Sector
Akriti Taneja,
Gayathri Nair,
Manisha Joshi,
Somesh Sharma,
Surabhi Sharma,
Anet Rezek Jambrak,
Elena Roselló-Soto,
Francisco J. Barba,
Juan M. Castagnini,
Noppol Leksawasdi,
Yuthana Phimolsiripol
Affiliations
Akriti Taneja
School of Bioengineering and Food Technology, Shoolini University, Solan 173229, Himachal Pradesh, India
Gayathri Nair
School of Bioengineering and Food Technology, Shoolini University, Solan 173229, Himachal Pradesh, India
Manisha Joshi
School of Bioengineering and Food Technology, Shoolini University, Solan 173229, Himachal Pradesh, India
Somesh Sharma
School of Bioengineering and Food Technology, Shoolini University, Solan 173229, Himachal Pradesh, India
Surabhi Sharma
School of Agricultural Science and Technology, RIMT, Fatehgarh Sahib 147301, Punjab, India
Anet Rezek Jambrak
Faculty of Food Technology and Biotechnology, University of Zagreb, 10000 Zagreb, Croatia
Elena Roselló-Soto
Department of Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine, Faculty of Pharmacy, Universitat de València, Avda. Vicent Andrés Estellés s/n, 46100 Burjassot, Spain
Francisco J. Barba
Department of Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine, Faculty of Pharmacy, Universitat de València, Avda. Vicent Andrés Estellés s/n, 46100 Burjassot, Spain
Juan M. Castagnini
Department of Preventive Medicine and Public Health, Food Science, Toxicology and Forensic Medicine, Faculty of Pharmacy, Universitat de València, Avda. Vicent Andrés Estellés s/n, 46100 Burjassot, Spain
Noppol Leksawasdi
Faculty of Agro-Industry, Chiang Mai University, Chiang Mai 50100, Thailand
Yuthana Phimolsiripol
Faculty of Agro-Industry, Chiang Mai University, Chiang Mai 50100, Thailand
Artificial intelligence (AI) involves the development of algorithms and computational models that enable machines to process and analyze large amounts of data, identify patterns and relationships, and make predictions or decisions based on that analysis. AI has become increasingly pervasive across a wide range of industries and sectors, with healthcare, finance, transportation, manufacturing, retail, education, and agriculture are a few examples to mention. As AI technology continues to advance, it is expected to have an even greater impact on industries in the future. For instance, AI is being increasingly used in the agri-food sector to improve productivity, efficiency, and sustainability. It has the potential to revolutionize the agri-food sector in several ways, including but not limited to precision agriculture, crop monitoring, predictive analytics, supply chain optimization, food processing, quality control, personalized nutrition, and food safety. This review emphasizes how recent developments in AI technology have transformed the agri-food sector by improving efficiency, reducing waste, and enhancing food safety and quality, providing particular examples. Furthermore, the challenges, limitations, and future prospects of AI in the field of food and agriculture are summarized.
