IoT- Enabled Firmness Grades of Tomato in Cold Supply Chain Using Fusion of Whale Optimization Algorithm and Extreme Learning Machine

Abstract

The assessment of tomato firmness is pivotal in determining optimal harvest time, evaluating shelf life, and gauging ripeness. This attribute plays a crucial role in guiding the distribution and transportation processes. Post-harvest, tomatoes tend to lose firmness and can deteriorate into a rotten state during transportation within the supply chain, mainly due to environmental fluctuations. To mitigate such losses and uphold tomato quality, the cold supply chain, with its controlled environmental conditions, proves instrumental. Monitoring this cold supply chain is imperative to combat the adverse impact of ambient temperatures on tomatoes during logistics. This research introduces an innovative approach, employing an Internet of Things (IoT) framework and the Whale Optimization Algorithm for temperature prediction within the cold supply chain. Ambient and tomato temperatures, along with stable temperature calculations under variable conditions using the Whale Optimization Algorithm, were collected. The predictions were executed using the Extreme Learning Machine of Artificial Intelligence. The data is collected during tomato cold storage for experimentation. The proposed technique with mean average precision 84.957%, mean average recall 96.9% and accuracy 99.83%. Evaluation through precision, recall, and F-measure accuracy metrics demonstrates the superior performance of the proposed approach compared to conventional models such as Decision Tree, Linear Model, Naïve Bays, Random Forest, and Support Vector Machine.

Keywords

• Artificial intelligence
• cold supply chain
• deep learning
• Internet of Things
• Newton’s law of cooling