Global warming is by far the most significant issue caused by climate change. Over the past few decades, heat stress has intensified into a serious issue that has a negative impact on crop production. Hence, it is crucial to modify cultivation systems to cope with this kind of stress, particularly in arid dry regions. In comparison to open-field cultivation, tomato production under protected cultivation techniques in walk-in tunnels that are suited for different farmers’ financial abilities was evaluated during the late summer season. The studied tunnels included a shaded net tunnel with natural ventilation, net tunnel with a fogging system and plastic tunnel with evaporative cooling (wet pad and fans). For the operation of fogging and evaporative cooling systems, solar energy was used as a sustainable, eco-friendly energy source. The results indicated that the solar energy system successfully operated the studied cooling systems. All studied protective cultivation techniques mitigated heat stress on tomato plant and improved the microclimate under walk-in tunnels. Moreover, evaporative cooling and fogging systems significantly increased plant leaf area, cell membrane efficiency and the contents of chlorophyll, relative water and proline compared to the net tunnel with natural ventilation. Furthermore, a marked reduction in physiological disorders was noticed. Improved physiological and biochemical parameters and limited physiological diseases led to higher fruit set, marketable fruit yield and total productivity. The percentage of marketable fruit yield increased by around 31.5% with an evaporative cooling system, 28.8% with a fogging system and 17% with a shaded net tunnel with no positive cooling as compared to an open field. However, the plants grown in open-field cultivation without protection significantly deteriorated from heat stress and had a high incidence of physiological disorders. The most incident physiological disorders were blossom-end rot, cracking, internal white tissues, sunscald, puffiness, blotchy ripening, cat face and exserted stigma. It is recommended to use a solar energy system to modify microclimate conditions through fogging or evaporative cooling under walk-in tunnels to ameliorate heat stress on grown tomato in the late summer season for higher fruit yield and fewer physiological disorders.

The harmful impacts of ozone (O₃) on plant development and productivity have been excessively studied. Furthermore, the positive influences of its low concentrations still need to be explored further. The present study was performed to assess the impact of low concentrations of O₃ on two sweet pepper hybrids under cold stress. The ozonated water was utilized for seed soaking or foliar application at concentrations of 0, 10, 20, 30, and 40 ppm. Seed soaking using ozonated water for 1 h was compared to soaking in distilled water as a control. Moreover, exogenously ozonated water was sprayed thrice at three-day intervals compared with untreated control. The differences between the applied methods (seed soaking and foliar application using ozonated water) were not statistically detected in most of the evaluated parameters. On the other hand, the evaluated hybrids displayed significant differences in the studied parameters, with the superiority of the Lirica evident in most germination and seedling growth parameters. Both applied methods significantly improved germination and seedling growth parameters. In particular, the concentration of 40 ppm displayed the highest enhancement of the germination index, coefficient velocity, and seedling quality. In addition, it promoted the seedling maintenance of high relative water content (RWC), chlorophyll, proline, and ascorbate peroxidase activity under cold stress conditions. Moreover, it protected the cell wall from damage by decreasing membrane permeability (MP). Generally, the best results were obtained from 40 ppm followed by 30 ppm of O₃ as seed soaking or foliar spray. The results pointed out the possible use of O₃ in a low concentration to protect the plants from cold stress during germination and early plant growth.

Moisture and potassium deficiency are two of the main limiting variables for squash crop performance in many water-stressed places worldwide. If major output decreases are to be avoided, it is critical to detect signs of crop stress as early as possible in the growth cycle. Proximal remote sensing can be a reliable technique for offering a rapid and precise instrument and localized management tool. This study tested the ability of proximal hyperspectral remotely sensed data to predict squash traits in two successive seasons (spring and fall) with varying moisture and potassium rates. Spectral data were collected from drip-irrigated squash that had been treated to varied rates of irrigation and potassium fertilization over both investigated seasons. To forecast potassium-use efficiency (KUE), chlorophyll meter (Chlm), water-use efficiency (WUE), and seed yield (SY) of squash, different commonly used and newly-introduced spectral index values for three bands (3D-SRIs), as well as a Decision Tree (DT) model, were evaluated. The results revealed that the newly constructed three-band SRIs based on the wavelengths of the visible (VIS), near-infrared (NIR), and red-edge regions were sensitive enough to measure the four tested parameters of squash in this study. For instance, NDI_558,646,708 presented the highest R² of 0.75 for KUE, NDI_744,746,738 presented the highest R² of 0.65 for Chlm, and NDI_670,628,392 presented the highest R² of 0.64 for SY of squash. The results further demonstrated that the principal component analysis (PCA) demonstrated the ability to distinguish moisture stress from potassium deficiency stress at the flowering stage onwards. Combining 3D-SRIs, DT-based bands (DT-b), and the aggregate of all spectral characteristics (ASF) with DT models would be an effective strategy for estimating four observed parameters with appropriate accuracy. For example, the model’s approximately 30 spectral characteristics were extremely important for predicting KUE. Its outputs with R² were, for the training and validation datasets, 0.967 (RMSE = 0.175) and 0.818 (RMSE = 0.284), respectively. For measuring Chlm, the DT-DT-b-20 model demonstrated the best. In the training and validation datasets, the R² value was 0.993 (RMSE = 0.522) and 0.692 (RMSE = 2.321), respectively. The overall outcomes showed that proximal-reflectance-sensing-based 3D-SRIs and DT models based on 3D-SRIs, DT-b, and ASF could be used to evaluate the four tested parameters of squash under different levels of irrigation regimes and potassium fertilizer.

Ozone is an important air pollutant that causes many challenges for human health, such as lung diseases. The negative impacts of exogenous ozone on cultivated plants have been discussed in several publications, but the positive impacts are less investigated. The current study is an attempt to answer the following question: is there any positive contribution of ozone pretreatment in growing plants under stress? Plants grown in unheated plastic greenhouses suffer from cold stress during the winter when the temperature drops to 5–10 °C. This stress can also be enhanced under saline soil conditions in arid regions. Treatments involving different ozone application methods (seed priming and seedling foliar application) and cold pretreatment (4 °C for 36 h) were compared with untreated controls of two sweet pepper hybrids (Zidenka and Lirica) in an unheated plastic greenhouse. In general, the Lirica hybrid outperformed the Zidenka hybrid in growth and fruit yield and showed good adaptation to cold stress resulting from ozone treatment through foliar application on seedlings. Moreover, cold treatment was associated with higher values of the studied parameters compared to the control (untreated). A concentration of 20 ppm of ozone in the first season and as 30 ppm of ozone in the second season increased plant leaf area, proline content, and APX activity, all of which improved the plant’s defense mechanisms against cold stress. These benefits contributed to high fruit sets; early, total, and marketable fruit yields; and fruit quality under cold stress. The highest yield (16.52 kg m⁻²) was attained with 30 ppm ozone applied as a seedling spray, compared with 10.07 kg m⁻² in the control. Therefore, the foliar application of ozone up to 30 ppm can be recommended for sweet pepper hybrids of Lirica under unheated plastic greenhouse conditions. Further investigations are needed to study the interaction of combined ozone and cold stress, as well as various levels of soil salinity.

Major international nutrition organizations are becoming increasingly concerned about global agriculture production. In particular, food insecurity has emerged in Egypt as a result of population expansion and rising food demand. Climate change and its variability are significant factors to the worldwide food insecurity problem. Therefore, cutting-edge techniques like IOT and machine learning which have shown its worth in several ways are needed by farmers and agricultural decision-makers to assist it in making timely judgments that will affect the quality of agricultural harvests. A novel open-source technology based on Arduino Board has been developed to predict onion crop irrigation needs. By utilizing three irrigation levels (100%, 85%, and 70% ETc) and integrating machine learning, the system predicts onion output in Talkha city, Egypt for 2023/2024. Meteorological and agronomic data are combined to aid farmers and decision-makers in predicting seasonal onion yields. Our system was constructed using Artificial Neural Network (ANN), Random Forest (RF), and Decision trees (DT) models. We found that, Using Arduino device at T85% and T70% helps to rationalize amount of water by 13% and 28% comparing with T100%. Furthermore, Arduino design with algorithm model gain a good value for (CWP) by (14.5 and 16 kg/m3) at T85% and T70%. Additionally, the results indicated that the ANN model works well during the testing phase, with a R2 of 94.3%, while the RF model and DT perform 90% and 86.1%, respectively. Our results showcase the effectiveness of the technology in enhancing agricultural decision-making and crop management.