Management of Intelligent Detection and Growth Cycle Prediction Model for Fruit and Vegetable Growth Based on Deep Learning

The goal of this research is to create an advanced prediction and forecasting system for fruits and vegetables using deep learning to enhance agricultural production management efficiency and accuracy. By combining a convolutional neural network (CNN) and a long short-term memory network (LSTM), an accurate prediction of the ripening stages of fruits and vegetables is achieved by leveraging image data and environmental conditions. The proposed model attains an accuracy prediction of $\mathbf{9 4. 5 \%}$ in the trial findings, along with a system response time of 2.3 seconds and a user satisfaction rate of $\mathbf{8 7. 2 \%}$. Moreover, the design of the system includes data collection, data analysis, predictive modeling, and user interaction functionalities to maintain system scalability and reliability. This research offers both a scientific answer for monitoring fruit and vegetable growth and technical assistance for future smart agricultural management, showcasing the potential of deep learning technology in agriculture.