The global average potato (Solanum tuberosum L.) production is 17.4 t ha-1. Even using the same potato varieties, there is a large gap between higher yields and lower yields among countries. Potatoes are a cash crop and a significant part of the global diet. Therefore, low soil fertility, soil-borne diseases, poor water quality, and pests seriously affect potato production in developing countries. To improve potato quality and production, it is necessary for the modern world to improve its potato cultivation techniques. Aeroponics cultivation is an alternative technology of soilless culture for effectively adapting to areas of the world where soil and water are in critical condition. In aeroponic systems, plant roots are suspended in the open air under controlled circumstances to replace the soil with artificially provided foam or plastic stents. Moreover, the nutrient solution is spread through atomization nozzles. This review provides insights into the potential use of aeroponics in complementing potato production in developing countries. Moreover, in most developing countries, this technology should be adopted after deliberate consideration to increase potato production.

Traditionally, crops are cultivated in soil-based open field systems. Seasonality, environmental degradation, urbanization, and food security issues have replaced open-field systems with modern plant production systems. Soilless culture is one of the modern plant production systems, which involves much higher use of available resources. The presented study provides information about currently accessible soilless systems and discussed the aeroponic system. Compared to other soilless systems, aeroponic reduce water usage through continuous water circulation. However, the aeroponic is not entirely implemented among local farmers, and very few farmers have adopted the system due to the lack of research and technical information available in the literature. Therefore, this study was planned to provide information about the development and maintenance tasks required for practicing the aeroponic system. This study could provide knowledge to the researchers, farmers, and those people interested in practicing the aeroponic system.

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.

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