Abstract:
Tomatoes export plays a vital role in agriculture economy of Pakistan. In 2021, Pakistan exported approximately 9.03 million USD worth of tomatoes to different countries. One major hurdle in tomatoes export is its sensitivity towards varying climate conditions as it directly affects the storage life of tomatoes, thus affecting the export's quality and market value. Utilizing the food supply wisely and minimizing food waste are essential for ensuring better exports and food security. In this research, proposed an artificial intelligence (AI) based system to detect the freshness and early ripeness of storage tomatoes. Our proposed system analyzes the real time storage conditions and helps in predicting ripening stages of tomatoes. The data acquired from temperature sensor, humidity sensor, ethylene gas sensor and methane gas sensor has analyzed by the machine learning models (SVM, Decision Tree, Random Forest and Logistic Regression) and timely predicts the repining stages of tomatoes with accuracy (91.5%, 91.3%, 90% and 80.5%) to assist in effective and informed timely decisions making. Early detection of tomatoes ripening also helps to reduce the spoilage, thus, benefiting both in terms of economy and food insecurity.
Page(s):
85-85
DOI:
DOI not available
Published:
Journal: Abstract Book on International Conference on Food and Applied Sciences (ICFAS-23) 3-5 August 23, Volume: 0, Issue: 0, Year: 2023