A Crop Harvest Time Prediction Model for Better Sustainability, Integrating Feature Selection and Artificial Intelligence Methods

Making an accurate crop harvest time prediction is a challenge for agricultural management. Previous studies of crop harvest time prediction were mainly based on statistical methods, and the features (variables) affecting it were determined by experience, resulting in its inaccuracy. To overcome these drawbacks, the objective of this paper is to develop a novel crop harvest time prediction model integrating feature selection and artificial intelligence (long short-term memory) methods based on real production and climate-related data in order to accurately predict harvest time and reduce resource waste for better sustainability. The model integrates a hybrid search for feature selection to identify features (variables) that can effectively represent input features (variables) first. Then, a long short-term memory model taking the selected features (variables) as input is used for harvest time prediction. A practical case (a large fruit and vegetable cooperative) is used to validate the proposed method. The results show that the proposed method (root mean square error (RMSE) = 0.199, mean absolute percentage error (MAPE) = 4.84%) is better than long short-term memory (RMSE = 0.565; MAPE = 15.92%) and recurrent neural networks (RMSE = 1.327; MAPE = 28.89%). Moreover, the nearer the harvest time, the better the prediction accuracy. The RMSE values for the prediction times of one week to harvesting period, two weeks to harvesting period, three weeks to harvesting period, and four weeks to harvesting period are 0.165, 0.185, 0.205, and 0.222, respectively. Compared with other existing studies, the proposed crop harvest time prediction model, LSTMFS, proves to be an effective method.

Automated summary

This study aims to develop a novel crop harvest time prediction model integrating feature selection and artificial intelligence methods to accurately predict harvest time and reduce resource waste. The results show that the proposed model is more accurate than traditional methods, and prediction accuracy improves as the harvest time approaches.