Application research of the improved Apriori algorithm based on frequent itemsets in intelligent greenhouses

作者：Yan Wen, Min Li, Yu Ye · 年份：2024 · DOI：10.1117/12.3026704 · 研究领域：Wireless Sensor Networks and IoT

In the promotion and application of smart agriculture, how can we reasonably utilize and process the massive environmental data acquired from the growth process of crops by smart terminal devices? This is key to whether smart agriculture can be widely applied and promoted. This paper applies the Apriori algorithm to analyze the data of the crop production process. The itemsets generated by the Apriori algorithm use the frequent itemset mining algorithm for reduction. Based on this, the Apriori algorithm is improved by optimizing specific cluster partitioning and parallel algorithms, thus reducing the time and space wastage of the traditional Apriori algorithm. Finally, the improved Apriori algorithm based on frequent itemsets is applied and validated in the intelligent greenhouse of Chengdu Agricultural Science and Technology Vocational College. Using data mining techniques, the correlation between parameters such as temperature during the growth process of greenhouse vegetables and vegetable yield is unearthed. This provides intelligent decision-making and scientific basis for the precision management of vegetable production in the greenhouse, thereby achieving the goal of increasing vegetable yields.