Plant health is the basis of agricultural development. Plant diseases are a major factor for crop losses in agriculture. Plant diseases are difficult to diagnose correctly, and the manual disease diagnosis process is time consuming. For this reason, it is highly desirable to automatically identify the diseases in strawberry plants to prevent loss of crop quality. Deep learning (DL) has recently gained popularity in image classification and identification due to its high accuracy and fast learning. In this research, deep learning models were used to identify the leaf scorch disease in strawberry plants. Four convolutional neural networks (SqueezeNet, EfficientNet-B3, VGG-16 and AlexNet) CNN models were trained and tested for the classification of healthy and leaf scorch disease infected plants. The performance accuracy of EfficientNet-B3 and VGG-16 was higher for the initial and severe stage of leaf scorch disease identification as compared to AlexNet and SqueezeNet. It was also observed that the severe disease (leaf scorch) stage was correctly classified more often than the initial stage of the disease. All the trained CNN models were integrated with a machine vision system for real-time image acquisition under two different lighting situations (natural and controlled) and identification of leaf scorch disease in strawberry plants. The field experiment results with controlled lightening arrangements, showed that the model EfficientNet-B3 achieved the highest classification accuracy, with 0.80 and 0.86 for initial and severe disease stages, respectively, in real-time. AlexNet achieved slightly lower validation accuracy (0.72, 0.79) in comparison with VGGNet and EfficientNet-B3. Experimental results stated that trained CNN models could be used in conjunction with variable rate agrochemical spraying systems, which will help farmers to reduce agrochemical use, crop input costs and environmental contamination.

The atomized nutrient solution droplet sizes and spraying intervals can impact the chemical properties of the nutrient solution, biomass yield, root-to-shoot ratio and nutrient uptake of aeroponically cultivated plants. In this study, four different nozzles having droplet sizes N1 = 11.24, N2 = 26.35, N3 = 17.38 and N4 = 4.89 µm were selected and misted at three nutrient solution spraying intervals of 30, 45 and 60 min, with a 5 min spraying time. The measured parameters were power of hydrogen (pH) and electrical conductivity (EC) values of the nutrient solution, shoot and root growth, ratio of roots to shoots (fresh and dry), biomass yield and nutrient uptake. The results indicated that the N1 presented significantly lower changes in chemical properties than those of N2, N3 and N4, resulting in stable lateral root growth and increased biomass yield. Also, the root-to-shoot ratio significantly increased with increasing spraying interval using N1 and N4 nozzles. The N1 nozzle also revealed a significant effect on the phosphorous, potassium and magnesium uptake by the plants misted at proposed nutrient solution spraying intervals. However, the ultrasonic nozzle showed a nonsignificant effect on all measured parameters with respect to spraying intervals. In the last, this research experiment validates the applicability of air-assisted nozzle (N1) misting at a 30-min spraying interval and 5 min of spraying time for the cultivation of butter-head lettuce in aeroponic systems.

Sustainable agriculture faces major issues with resource efficiency, nutrient distribution, and plant health. Traditional soil-based and soilless farming systems encounter issues including excessive water use, insufficient nutrient uptake, nitrogen deficiency, and restricted plant development. According to the previous literature, aeroponic systems accelerate plant growth rates, improve root oxygenation, and significantly enhance water use efficiency, particularly when paired with both low- and high-pressure misting systems. However, despite these advantages, they also present certain challenges. A major drawback is the inefficiency of nitrogen fixation, resulting in insufficient nutrient availability and heightened plant stress from uncontrolled misting, which ultimately reduces yield. Many studies have investigated plasma uses in both soil-based and soilless plant cultures; nevertheless, however, its function in aeroponics remains unexplored. Therefore, the present work aims to thoroughly investigate and review the integration of plasma-activated water (PAW) and plasma-activated mist (PAM) in aeroponics systems to solve important problems. A review of the current literature discloses that PAW and PAM expand nitrogen fixation, promote nutrient efficiency, and modulate microbial populations, resulting in elevated crop yields and enhanced plant health, akin to soil-based and other soilless systems. Reactive oxygen and nitrogen species (RONS) produced by plasma treatments improve nutrient bioavailability, root development, and microbial equilibrium, alleviating critical challenges in aeroponics, especially within fine-mist settings. This review further examines artificial intelligence (AI) and the Internet of Things (IoT) in aeroponics. Models driven by AI enable the accurate regulation of fertilizer concentrations, misting cycles, temperature, and humidity, as well as real-time monitoring and predictive analytics. IoT-enabled smart farming systems employ sensors for continuous nutrient monitoring and gas detection (e.g., NO₂, O₃, NH₃), providing automated modifications to enhance aeroponic efficiency. Based on a brief review of the current literature, this study concludes that the future integration of plasma technology with AI and IoT may address the limitations of aeroponics. The integration of plasma technology with intelligent misting and data-driven control systems can enhance aeroponic systems for sustainable and efficient agricultural production. This research supports the existing body of research that advocates for plasma-based innovations and intelligent agricultural solutions in precision farming.

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.

Influences of moisture content and compressive loading speed on the mechanical properties of maize grain orientations

Various rapid propagation strategies have been discovered, which has facilitated large-scale plant reproduction and cultivar development. These methods, in many plant species, are used to rapidly generate large quantities (900 mini-tubers/m²) of high-quality propagule (free from contamination) at a relatively low cost in a small space. They are also used for plant preservation. This review article aims to provide potential applications for regeneration and clonal propagation. Plant propagation using advanced agrotechnology, such as aeroponics, is becoming increasingly popular among academics and industrialists. The advancement of asexual aeroponic propagation has been achieved through advancements in monitoring and control systems using IoT and smart sensor technology. New sensor technology systems have gained substantial interest in agriculture in recent years. It is used in agriculture to precisely arrange various operations and objectives while harnessing limited resources with minimal human intervention. Modern intelligent technologies and control systems simplify sensor data collection, making it more efficient than manual data collection, which can be slow and prone to errors. Specific ambient variables like temperature, humidity, light intensity, stock solution concentrations (nutrient water), EC (electrical conductivity), pH values, CO₂ content, and atomization parameters (frequency and interval) are collected more effectively through these systems. The use of intelligent technologies provides complete control over the system. When combined with IoT, it aids in boosting crop quality and yield while also lowering production costs and providing data directly to tablets and smartphones in aeroponic propagation systems. It can potentially increase the system’s productivity and usefulness compared to the older manual monitoring and operating methods.