A Review of Using Artificial Intelligence and Machine Learning in Food and Agriculture Industry

Pallathadka, Harikumar; Jawarneh, Malik; Sammy, F.; Garchar, Vipul; Sanchez, Domenic T.; Naved, Mohd

doi:10.1109/icacite53722.2022.9823427

Cited by 21 publications

(2 citation statements)

References 19 publications

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“…The challenge is to adapt these elements to the traditional conceptual model of the institution, seeking not only to obtain results in terms of creativity and cooperation, but also to reflect and adapt the educational model to the current and future needs of the business environment. (26,27,28) The implementation of this curricular proposal represents an important step forward for Fundación Universitaria San Mateo. By integrating AI into its International Business program, the institution positions itself at the forefront of business education, preparing its students to meet the challenges of the 21st century with a solid foundation in technology, data analytics and a deep understanding of international logistics.…”

Section: Discussionmentioning

confidence: 99%

Logistics 4.0: Exploring Artificial Intelligence Trends in Efficient Supply Chain Management

Albarracín Vanoy

2023

Data and Metadata

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Introduction: In the current era of globalization and digitalization, international logistics faces unique challenges and opportunities. The growing demand for efficient supply chain management, combined with the need to reduce costs and improve services, has led to the adoption of advanced technologies such as Artificial Intelligence (AI). AI has become a key catalyst in the transformation of logistics, giving way to what is known as Logistics 4.0. This paper explores the most recent trends of AI in international logistics and its integration into education, with a specific focus on the San Mateo University Foundation.Methods: This mixed study, combining qualitative and quantitative methods, begins with quantitative data collection and analysis, followed by a qualitative phase. The qualitative approach focuses on students' perceptions of logistics training, while the quantitative approach describes how they perceive AI tools. The research included students and companies in Bogota, analyzing their familiarity with AI and its implementation in practice.Results: The findings indicate that AI is increasingly relevant in logistics, especially in process automation and data-driven decision making. Most companies surveyed have a good understanding of AI, but less than half implement it in their operations. Students recognize the importance of AI in logistics and its positive impact on education. There is consensus on the role of AI in improving educational quality, highlighting its usefulness in optimizing processes and personalizing learning.Conclusions: The research highlights the crucial role of AI in modern logistics and its ability to improve operational efficiency. The integration of AI in international business education is critical to enrich students' learning experience and prepare them for the challenges of the labor market. The blended methodology used is effective in gaining a holistic view of AI integration in logistics and its educational impact. The conclusions provide guidelines for curriculum development in international business with a focus on international logistics, aligning curricula with emerging trends in logistics and AI.

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Section: Discussionmentioning

confidence: 99%

Logistics 4.0: Exploring Artificial Intelligence Trends in Efficient Supply Chain Management

Albarracín Vanoy

2023

Data and Metadata

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“…Due to this feature, machine learning has rapidly become the fundamental technique of many modern applications, including computer vision, natural language processing (NLP), fraud detection, medical analysis (both physically and mentally), the agriculture industry, the energy sector, mechanical engineering, network security, etc. [3][4][5][6][7][8][9][10][11][12]. Machine learning has spawned many branches, such as supervised learning and unsupervised learning.…”

Section: Introductionmentioning

confidence: 99%

FocalMatch: Mitigating Class Imbalance of Pseudo Labels in Semi-Supervised Learning

Deng

Zhang²,

Yang³

et al. 2022

Applied Sciences

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Semi-supervised learning (SSL) is a popular research area in machine learning which utilizes both labeled and unlabeled data. As an important method for the generation of artificial hard labels for unlabeled data, the pseudo-labeling method is introduced by applying a high and fixed threshold in most state-of-the-art SSL models. However, early models prefer certain classes that are easy to learn, which results in a high-skewed class imbalance in the generated hard labels. The class imbalance will lead to less effective learning of other minority classes and slower convergence for the training model. The aim of this paper is to mitigate the performance degradation caused by class imbalance and gradually reduce the class imbalance in the unsupervised part. To achieve this objective, we propose FocalMatch, a novel SSL method that combines FixMatch and focal loss. Our contribution of FocalMatch adjusts the loss weight of various data depending on how well their predictions match up with their pseudo labels, which can accelerate system learning and model convergence and achieve state-of-the-art performance on several semi-supervised learning benchmarks. Particularly, its effectiveness is demonstrated with the dataset that has extremely limited labeled data.

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Hunger games search optimization with deep learning model for sustainable supply chain management

Xu,

Jain,

Neelakandan

et al. 2023

Discov Internet Things

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The supply chain network is one of the most important areas of focus in the majority of business circumstances. Blockchain technology is a feasible choice for secure information sharing in a supply chain network. Despite the fact that maintaining security at all levels of the blockchain is difficult, cryptographic methods are commonly used in existing works. Effective supply chain management (SCM) offers various benefits to organizations, such as enhanced customer satisfaction, increased operational efficiency, competitive advantage, and cost reduction. Potential SCM for agricultural and food supply chains needs distributors, coordination and collaboration among farmers, retailers, and stakeholders. The use of technology like Block Chain (BC), sensors, and data analytics, can boost traceability and visibility, decrease waste, and ensure safety and quality throughout the supply chain. Therefore, this study develops a Hunger Games Search Optimization with Deep Learning Model for Sustainable agricultural and food Supply Chain Management (HGSODL-ASCM) technique. The fundamental goal of the HGSODL-ASCM technique is to improve decision-making processes for agricultural and food commodity production and storage in order to optimise revenue. In the provided HGSODL-ASCM technique, a bidirectional long short-term memory (Bi-LSTM) model is built to determine the amount of productivity and storage required to maximise profit. In order to boost the performance of the Bi-LSTM classification process, the HGSO algorithm has been utilized in this work. The presented HGSODL-ASCM technique can independently achieve the SCM policies via interaction with complicated and adaptive environments. A brief set of simulations were performed to ensure the improved performance of the HGSODL-ASCM technique. The simulation results demonstrated how superior the HGSODL-ASCM method is to other methods already in use.

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A Review of Using Artificial Intelligence and Machine Learning in Food and Agriculture Industry

Cited by 21 publications

References 19 publications

Logistics 4.0: Exploring Artificial Intelligence Trends in Efficient Supply Chain Management

Logistics 4.0: Exploring Artificial Intelligence Trends in Efficient Supply Chain Management

FocalMatch: Mitigating Class Imbalance of Pseudo Labels in Semi-Supervised Learning

Hunger games search optimization with deep learning model for sustainable supply chain management

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