Prediction of Maturity Date of Leafy Greens Based on Causal Inference and Convolutional Neural Network

Abstract

The prediction of the maturity date of leafy greens in a planting environment is an essential research direction of precision agriculture. Real-time detection of crop growth status and prediction of its maturity for harvesting is of great significance for improving the management of greenhouse crops and improving the quality and efficiency of the greenhouse planting industry. The development of image processing technology provides great help for real-time monitoring of crop growth. However, image processing technology can only obtain the representation information of leafy greens, and it is difficult to describe the causal mechanism of environmental factors affecting crop growth. Therefore, a framework combining an image processing model and a crop growth model based on causal inference was proposed to predict the maturity of leafy greens. In this paper, a deep convolutional neural network was used to classify the growth stages of leafy greens. Then, since some environmental factors have causal effects on the growth rate of leafy greens, the causal effects of various environmental factors on the growth of leafy greens are obtained according to the data recorded by environmental sensors in the greenhouse, and the prediction results of the maturity of leafy greens in the study area are obtained by combining image data. The experiments showed that the root mean square error (RMSE) was 2.49 days, which demonstrated that the method had substantial feasibility in predicting the maturity for harvesting and effectively solved the limitations of poor timeliness of prediction. This model has great application potential in predicting crop maturity in greenhouses.

A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture

“…However, classification accuracy was lower for immature peppers, warranting further research into data augmentation and transfer learning to improve model robustness. In another study, Shi et al [135] combined deep learning and causal analysis to predict maturity dates of leafy greens in greenhouses, achieving a root mean squared error of only 2.49 days. While novel, the approach struggled with crops in late static growth stages, suggesting the need for adaptive models that emphasize historical data over static phenotypes in late stages.…”