Intelligent Planting of Subtropical Fruits and Vegetables based on Artificial Intelligence

Abstract

This thesis discusses the intelligent planting system of subtropical fruits and vegetables based on artificial intelligence (AI) and visual recognition, especially the application in water and fertilizer management. In view of the problems of inaccurate and inefficient water and fertilizer management faced by fruit and vegetable planting in subtropical areas, this study implements an intelligent water and fertilizer decision support system, which integrates visual recognition, sensor data acquisition, machine learning algorithms and other technologies. Through visual recognition technology, the system can monitor the growth status of crops in real time, accurately identify crop growth parameters and pests and diseases, and provide data support for water and fertilizer decisions. Combined with sensor data such as soil moisture and nutrient content, machine learning algorithms are used to predict and analyze crop water and fertilizer requirements to achieve accurate water and fertilizer management. The experimental results show that the system can significantly improve the accuracy and efficiency of water and fertilizer management and promote high-quality and high-yield subtropical fruits and vegetables. This study provides new ideas and technical support for intelligent and precise agriculture, and has important application value and promotion prospects.