A Study on Sugar Content Improvement and Distribution Flow Response through Citrus Sugar Content Prediction Based on the PyCaret Library

Abstract

Despite the increasing attention on smart farms as a solution to rural issues such as aging agricultural populations, a shortage of young farmers, decreased production area, and reduced investment leading to stagnant income, exports, and growth rates, many farms still rely on traditional methods like cultivating tangerines in open fields. Despite this, increasing farm income requires producing high-quality tangerines and selling them at premium prices, with fruit sweetness being a crucial factor. Therefore, there is a need to examine the close correlation between tangerine quality and sweetness. In this paper, we use deep learning with the PyCaret library to predict and analyze tangerine sweetness using data from seven regions in Jeju and 13 comprehensive factors influencing sweetness, including terrain, temperature, humidity, precipitation, sunlight, wind speed, acidity, sugar-acid ratio, and others. Although applying all 13 factors could achieve over 90% accuracy, our study, limited to seven factors, still achieves a respectable 82.4% prediction accuracy, demonstrating the significant impact of weather data on sweetness. Moreover, these optimistic predictions enable the estimation of tangerine quality and price formation in the market for the coming year, allowing tangerine farmers and related agencies to respond to market conditions proactively. Furthermore, by applying these data to smart farms to control factors influencing tangerine sweetness, it is anticipated that high-quality tangerine production and increased farm income can be achieved.

Keywords

• tangerine production factors
• quality
• sweetness
• PyCaret
• deep learning
• data analysis