Assistant Professor Dr Hemantha Kumar Kalluri from the Department of Computer Science and Engineering and post-doctoral fellow, Dr Premkumar Borugadda have published a research paper titled, A Comprehensive Analysis of Artificial Intelligence, Machine Learning, Deep Learning and Computer Vision in Food Science. This significant research explores how Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Computer Vision (CV) are making food processing smarter and more reliable.
Here’s a brief on their findings and the social and practical implementations of their work.
A Brief Abstract
Providing safe and quality food is crucial for every household and is of extreme significance in the growth of any society. It is a complex procedure that deals with all issues focusing on the development of food processing from seed to harvest, storage, preparation, and consumption. This current paper seeks to demystify the importance of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Computer Vision (CV) in ensuring food safety and quality. By stressing the importance of these technologies, the audience will feel reassured and confident in their potential. These are very handy for such problems, giving assurance over food safety. CV is incredibly noble in today’s generation because it improves food processing quality and positively impacts firms and researchers. Thus, at the present production stage, rich in image processing and computer visioning is incorporated into all facets of food production. In this field, DL and ML are implemented to identify the type of food in addition to quality. Concerning data and result-oriented perceptions, one has found similarities regarding various approaches. As a result, the findings of this study will be helpful for scholars looking for a proper approach to identify the quality of food offered. It helps to indicate which food products have been discussed by other scholars and lets the reader know papers by other scholars inclined to research further. Also, deep learning is accurately integrated with identifying the quality and safety of foods in the market. This paper describes the current practices and concerns of ML, DL, and probable trends for its future development.
Explanation of the Research in Layperson’s Terms
The research explores how Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Computer Vision (CV) are making food processing smarter and more reliable.
AI and ML in Food Processing
Computer Vision (CV) for Food Inspection
Deep Learning for Better Food Safety
Future of Smart Food Processing
Practical Implementation and Social Implications
The research on Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Computer Vision (CV) in Food Science has significant real-world applications and social implications:
Practical Implementation
Our research has directed the researchers to develop applications in various stages of the food industry, from agriculture to food processing, quality control, and distribution. Here are some key practical implementations:
1.1 Computer Vision (CV) & Deep Learning (DL) for Defect Detection
1.2 AI for Food Adulteration Detection
2. AI in Food Safety and Hygiene Monitoring
2.1 AI-based Sensors for Real-time Food Safety Checks
This research bridges the gap between technology and food security, ensuring that AI and ML can revolutionise the way food is produced, processed, and consumed. These technologies enhance quality control, reduce food waste, ensure hygiene, and support sustainable agriculture, leading to a healthier, safer, and more efficient global food system.
Future Research Plans.
AI-Powered Automated Food Sorting & Grading
Dr Vemula Dinesh Reddy, Assistant Professor, Department of Computer Science and Engineering, has been granted a patent for his invention “A System And A Method for Fog-Based Animal Intrusion Detection” with the Application No: 202341026013, in the Indian Patent Official Journal. The invention acts as a groundbreaking fog computing-based system designed for real-time detection of animal intrusions in sensitive areas using smart sensors for instant alerts.
Abstract
This research introduces an intelligent system using fog computing to detect animal intrusions in sensitive or protected zones such as farmlands, highways, and forest borders. The system enables real-time data processing closer to the site of intrusion, offering faster detection and reduced dependency on centralised cloud systems. Furthermore, we proposed the Quantum-Inspired optimisation technique called Quantum Evolutionary Algorithm.
Practical Implementation/ Social Implications of the Research
Through this invention, we can:
Future Research Plans