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

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