Plant diseases affect the availability and safety of plants for human and animal consumption and threaten food safety, thus reducing food availability and access, as well as reducing crop yield and quality. There is a need for novel disease detection methods that can be used to reduce plant losses due to disease. Thus, this study aims to diagnose tomato leaf diseases by classifying healthy and unhealthy tomato leaf images using two pre-trained convolutional neural networks (CNNs): Inception V3 and Inception ResNet V2. The two models were trained using an open-source database (PlantVillage) and field-recorded images with a total of 5225 images. The models were investigated with dropout rates of 5%, 10%, 15%, 20%, 25%, 30%, 40%, and 50%. The most important results showed that the Inception V3 model with a 50% dropout rate and the Inception ResNet V2 model with a 15% dropout rate, as they gave the best performance with an accuracy of 99.22% and a loss of 0.03. The high-performance rate shows the possibility of utilizing CNNs models for diagnosing tomato diseases under field and laboratory conditions. It is also an approach that can be expanded to support an integrated system for diagnosing various plant diseases.

Urban areas face significant challenges, including a lack of green spaces, scarce water resources, environmental pollution, and elevated heat emissions, particularly in developing countries experiencing rapid population growth. Therefore, the study aims to advance sustainable urban agriculture by designing and evaluating a solar-powered smart rooftop irrigation system for peppermint cultivation. The system incorporates two drip irrigation setups—conventional and smart irrigation—powered by photovoltaic (PV) panels. The smart system integrates real-time monitoring of critical variables, including (1) soil moisture, (2) relative humidity, (3) PV panel temperature, and (4) PV panel current and voltage. Key performance metrics such as water and energy consumption, water use efficiency, energy productivity, and carbon dioxide emissions were evaluated for both systems. In addition, the economic analysis of the smart system was determined. Results revealed that the smart system reduced water and energy consumption by 28.1% compared to conventional irrigation. Additionally, the smart system achieved a notable reduction in carbon footprint, with CO₂ emissions of 0.181 kg CO₂/m²/year compared to 0.252 kg CO₂/m²/year for the conventional system. The system’s economic analysis demonstrated a payback period of 5.6 years, highlighting its financial viability. This study underscores the transformative potential of solar-powered smart irrigation systems in enhancing food security, conserving water, reducing energy consumption, and mitigating carbon emissions in urban agriculture.

Water scarcity is a major challenge for irrigated agriculture, particularly in developing countries where access to meteorological data for calculating reference evapotranspiration (ET_o) is limited. Thus, this study explores the potential of two machine learning models (random forest (RF) and long short-term memory (LSTM)) and autoregressive integrated moving average (ARIMA) to forecast ET_o. The investigation was conducted for four weather stations in Egypt, from 1982 to 2020. The machine learning models were evaluated using four combinations of inputs: maximum and minimum temperature, relative humidity, and wind speed. The best results for both RF and LSTM models were achieved with the first set of inputs that included all four variables at both regional and local scales. For the regional scale, RF and LSTM models achieved R² values of 0.85 and 0.86, respectively, with RMSE values of 0.69 and 0.68 mm/day. At the local scale, RF and LSTM models exhibited R² values ranging from 0.92 to 0.95 and 0.93 to 0.95, respectively, while RMSE ranged between 0.38 and 0.46 mm/day and 0.37–0.43 mm/day, respectively. Additionally, ARIMA models were employed for tim series analysis of the same ET_o data. ARIMA (2,1,4) and ARIMA (2,1,3) were found to be the most suitable models for the local-scale analysis while ARIMA (2,1,4) was identified as the optimal model for the regional-scale analysis. For the local-scale analysis, R² values ranged from 0.86 to 0.91 and RMSE values ranged from 0.26 to 0.38. The regional scale analysis yielded an R² value of 0.89 and an RMSE value of 0.58 mm/day. The developed models can be used in places where meteorological data for forecasting ET_o are limited.