Plant diseases cause significant damage to agriculture, leading to substantial yield losses and posing a major threat to food security. Detection, identification, quantification, and diagnosis of plant diseases are crucial parts of precision agriculture and crop protection. Modernizing agriculture and improving production efficiency are significantly affected by using computer vision technology for crop disease diagnosis. This technology is notable for its non-destructive nature, speed, real-time responsiveness, and precision. Deep learning (DL), a recent breakthrough in computer vision, has become a focal point in agricultural plant protection that can minimize the biases of manually selecting disease spot features. This study reviews the techniques and tools used for automatic disease identification, state-of-the-art DL models, and recent trends in DL-based image analysis. The techniques, performance, benefits, drawbacks, underlying frameworks, and reference datasets of more than 278 research articles were analyzed and subsequently highlighted in accordance with the architecture of computer vision and deep learning models. Key findings include the effectiveness of imaging techniques and sensors like RGB, multispectral, and hyperspectral cameras for early disease detection. Researchers also evaluated various DL architectures, such as convolutional neural networks, vision transformers, generative adversarial networks, vision language models, and foundation models. Moreover, the study connects academic research with practical agricultural applications, providing guidance on the suitability of these models for production environments. This comprehensive review offers valuable insights into the current state and future directions of deep learning in plant disease detection, making it a significant resource for researchers, academicians, and practitioners in precision agriculture.

In the coming years, climate change is predicted to impact irrigation water demand considerably, particularly in semi-arid regions. The aim of this research is to investigate the expected adverse impacts of climate change on water irrigation management in Saudi Arabia. We focus on the influence of climate change on irrigation water requirements in the Al Quassim (97,408 ha) region. Different climate models were used for the intermediate emission SSP2-4.5 and the high emission SSP5-8.5 Coupled Model Intercomparison Project Phase 6 (CMIP6) scenarios. The FAO-CROPWAT 8.0 model was used to calculate reference evapotranspiration (ETo) using weather data from 13 stations from 1991 to 2020 and for both the SSP2-4.5 and SSP5-8.5 scenarios for the 2040s, 2060s, 2080s, and 2100s. The findings indicated that, for the 2100s, the SSP2-4.5 and SSP5-8.5 scenarios forecast annual average ETo increases of 0.35 mm/d (6%) and 0.7 mm/d (12.0%), respectively. Net irrigation water requirement (NIWR) and growth of irrigation water requirement (GIWR) for the main crops in the Al Quassim region were assessed for the current, SSP2-4.5, and SSP5-8.5 scenarios. For SSP5-8.5, the GIWR for the 2040s, 2060s, 2080s, and 2100s are expected to increase by 2.7, 6.5, 8.5, and 12.4%, respectively, compared to the current scenario (1584.7 million m³). As a result, there will be higher deficits in 2100 under SSP5-8.5 for major crops, with deficits of 15.1%, 10.7%, 8.3%, 13.9%, and 10.7% in the crop areas of wheat, clover, maize, other vegetables, and dates, respectively. Optimal irrigation planning, crop pattern selection, and modern irrigation technologies, combined with the proposed NIWR values, can support water resources management. The findings can assist managers and policymakers in better identifying adaptation strategies for areas with similar climates.

Flash floods, rapid and devastating inundations of water, are increasingly linked to the intensifying effects of climate change, posing significant challenges for both vulnerable communities and sustainable environmental management. The primary goal of this research is to investigate and predict a Flood Susceptibility Map (FSM) for the Ibaraki prefecture in Japan. This research utilizes a Random Forest (RF) regression model and GIS, incorporating 11 environmental variables (involving elevation, slope, aspect, distance to stream, distance to river, distance to road, land cover, topographic wetness index, stream power index, and plan and profile curvature), alongside a dataset comprising 224 instances of flooded and non-flooded locations. The data was randomly classified into a 70 % training set for model development, with the remaining 30 % used for model validation through Receiver Operating Characteristics (ROC) curve analysis. The resulting map indicated that approximately two-thirds of the prefecture as exhibiting low to very low flood susceptibility, while approximately one-fifth of the region is categorized as high to very high flood susceptibility. Furthermore, the RF model achieved a noteworthy validation with an area under the ROC curve of 99.56 %. Ultimately, this FSM serves as a crucial tool for policymakers in guiding appropriate spatial planning and flood mitigation strategies.

Land-use/land-cover (LULC) change is considered a key human factor influencing groundwater recharge in floodplains. Without accurate estimations, the impact of LULC change on water balance components may be either significantly understated or exaggerated. This paper assesses the impacts of LULC changes from 1990 to 2018 on water balance components and groundwater levels of the Drava floodplain, Hungary, where human interference has led to a critical environmental situation. In this study, a spatially-distributed water balance model (WetSpass-M), and a groundwater flow model (MODFLOW-NWT) were integrated to assess the impacts of LULC changes. The moderate expansion of built-up areas increased surface runoff, while the afforestation of arable land and meadows and the overgrowth of bare mudflats with willow shrubs increased evapotranspiration. As a consequence, total annual groundwater recharge decreased by 5.3 × 10⁷ m³ in the floodplain with an average of 335 mm year⁻¹ and 317 mm year⁻¹ in 2012 and 2018, respectively. Moreover, an average groundwater level decline by 0.1 m is observed in the same period. Declined groundwater recharge, increased runoff, and evapotranspiration exerted a negative effect on water resources in the Drava basin. The approach tested in this paper allows temporal and spatial estimation of hydrological components under the changes of LULC, providing quantitative information for decision-makers and stakeholders to implement efficient and sustainable management of water resources in the Drava floodplain. The provided integrated model is also applicable to regionally.