Accurate detection of cutting diseases in the process of aeroponic rapid propagation is very important for improving the rooting rate and survival rate of cuttings. This paper proposes to use image processing, with a dataset of the growth of mulberry cuttings and a backward propagation (BP) neural network, to identify mildew on the roots of mulberry branches in the process of rapid propagation, before extracting texture and color features. An intelligent control aeroponics system was designed to control the ambient temperature and humidity of the entire rapid propagation incubator according to the mildew rate, thereby improving the rapid propagation time of aeroponics, as well as the rooting and survival rates. In order to distinguish the extracted features, they were classified and identified using a constructed BP neural network model. The results indicated that the performance of the neutral network showed the lowest mean square error in the validation set after three rounds of training; therefore, the model of the third round was chosen as the best model. Furthermore, the training effect of the model revealed that the BP neural network model had good stability and could accurately identify diseases in the root zone of mulberry cuttings. After using MATLAB for neural network training, the regression results revealed correlation coefficients R of 0.98 for the fitting curve of the training dataset, 0.98 for the fitting curve of the test set, and 0.99 for the fitting curve of the validation set, indicating that the prediction results aligned well with the actual results. It can be concluded that research method described in this paper had excellent performance in identifying the health status of mulberry cuttings during the aeroponics rapid propagation process, and it was able to quickly and accurately identify mulberry cuttings affected by mildew disease with an accuracy rate of 80%. This research provides a technical reference for aeroponics rapid propagation factories and intelligent nurseries.

Due to the scarcity of water, it is necessary to develop an environmentally friendly method for increasing water productivity and crop production. An experiment was conducted to assess the effects of different magnetic levels (magnetic water level 1 (MWL 1) = 3800 Gauss, magnetic water level 2 (MWL 2) = 5250 Gauss, and magnetic water level 3 (MWL 3) = 6300 Gauss, as well as normal water (NW) as a control) in combination with three soilless culture systems (a nutrient film technique (NFT) hydroponics system, a tower aeroponics system, and a pyramidal aeroponics system. The results showed that the utilization of magnetic water had significant effects on the yield and growth of strawberry plants The tower aeroponic system under MWL 3 produced the highest yield and water productivity, with increases of 80.9% and 89%, respectively, over the control. The tower aeroponic system under MWL 3 produced the highest yield and water productivity, with increases of 80.9% and 89%, respectively, over the control. In addition, as compared to the NW, the NFT system increased yield and water productivity by 71.1% and 79.3%, respectively, whilst the pyramidal system increased yield and water productivity by 66.87% and 82%, respectively. Furthermore, when compared to the control, the combination of the NFT system and magnetic water level 3 (MWL 3) resulted in the most leaves, largest stem diameter, and largest leaf area of the strawberry plants resulted in the most leaves, stem diameter, and leaf area of strawberry plants. In comparison to all other treatments, this combination produced the best fruit quality and yield, as well as its constituents, such as titratable acidity, total soluble solids, and fruit hardness. This study found that combining magnetic therapy with soilless culture techniques resulted in increased yield and water productivity. In addition, water and fertigation solution usage in the NFT, tower, and pyramidal systems dropped by 4.8%, 6%, and 4.8%, respectively. Furthermore, it enhanced plant morphology and plant quality.