Smart management of weather data is an essential step toward implementing sustainability and precision in agriculture. It represents an important input for numerous tasks, such as crop growth, development, yield, and irrigation scheduling, to name a few. Advances in technology allow collecting this weather data from heterogeneous sources with high temporal resolution and at low cost. Generating and using these data in their raw form makes no sense, and therefore implementing adequate infrastructure and tools is necessary. For that purpose, this paper presents a smart weather data management system evaluated using data from a meteorological station installed in our study area covering the period from 2013 to 2020 at a half-hourly scale. The proposed system makes use of state-of-the-art statistical methods, machine learning, and deep learning models to derive actionable insights from these raw data. The general architecture is made up of four layers: data acquisition, data storage, data processing, and application layers. The data sources include real-time sensors, IoT devices, reanalysis data, and raw files. The data are then checked for errors and missing values using a proposed method based on ERA5-Land reanalysis data and deep learning. The resulting coefficient of determination (R²) and Root Mean Squared Error (RMSE) for this method were 0.96 and 0.04, respectively, for the scaled air temperature estimate. The MongoDB NoSQL database is used for storage thanks to its ability to deal with real-world big data. The system offers various services such as (i) weather time series forecasts, (ii) visualization and analysis of meteorological data, and (iii) the use of machine learning to estimate the reference evapotranspiration (ET₀) needed for efficient irrigation. To this, the platform uses the XGBoost model to achieve the precision of the Penman–Monteith method while using a limited number of meteorological variables (air temperature and global solar radiation). Results for this approach give R² = 0.97 and RMSE = 0.07. This system represents the first incremental step toward implementing smart and sustainable agriculture in Morocco.

In this review, collected information related to Moringa Oleifera seeds was evaluated, such as their properties and the main active components involved in their processes, as well as their dual efficiency as both antimicrobials and natural coagulants for treating contaminated effluents. Furthermore, discussions were completed about perspectives on progress related to this field of research to understand the bioactive properties of these seed compounds, including their antibacterial, antifungal, and antiviral activity. In addition to the coagulant properties that have been quantitatively assessed, studies have examined the underlying coagulating mechanism, and seed processing techniques. In addition, the challenges associated with the use of conventional coagulants (metals or polymers) have led to numerous research efforts towards the development of natural plant-based coagulants that are eco-friendly to treat wastewater and offer a large variety of other advantages, such as their wide availability, the reduction of by-product generation, the reduction of costs, and greater biodegradability. Based on the results of different researchers, and regarding the appraisals using Moringa Oleifera seeds for wastewater treatment processes, many studies encourage their use for those operations. Due to their extensive and potent properties as an antibacterial and a coagulant, Moringa Oleifera seeds are still used today as a promising wastewater treatment method. Finally, this paper provides suggestions and comments, as well as identifies the knowledge gaps, and makes recommendations for future research development strategies, such as studying the contents of Moringa Oleifera seeds, their interactions with colloids present in wastewater, understanding their stability and behavior, assessing the performance of seed-derived flocculants according to pH values, isolating and characterizing the active compounds to determine the toxicity and optimum dose to be used as effective antimicrobials, and removing heavy metals.

Quickly and correctly mapping soil nutrients significantly impact accurate fertilization, food security, soil productivity, and sustainable agricultural development. We evaluated the potential of the new PRISMA hyperspectral sensor for mapping soil organic matter (SOM), available soil phosphorus (P₂O₅), and potassium (K₂O) content over a cultivated area in Khouribga, northern Morocco. These soil nutrients were estimated using (i) the random forest (RF) algorithm based on feature selection methods, including feature subset evaluation and feature ranking methods belonging to three categories (i.e., filter, wrapper, and embedded techniques), and (ii) 107 soil samples taken from the study area. The results show that the RF-embedded method produced better predictive accuracy compared with the filter and wrapper methods. The model for SOM showed moderate accuracy ($R_{val}^2$ = 0.5, RMSEP = 0.43%, and RPIQ = 2.02), whereas that for soil P₂O₅ and K₂O exhibited low efficiency ($R_{val}^2$ = 0.26 and 0.36, RMSEP = 51.07 and 182.31 ppm, RPIQ = 0.65 and 1.16, respectively). The interpolation of RF-residuals by ordinary kriging (OK) methods reached the highest predictive results for SOM ($R_{val}^2$ = 0.69, RMSEP = 0.34%, and RPIQ = 2.56), soil P₂O₅ ($R_{val}^2$ = 0.44, RMSEP = 44.10 ppm, and RPIQ = 0.75), and soil K₂O ($R_{val}^2$ = 0.51, RMSEP = 159.29 ppm, and RPIQ = 1.34), representing the best fitting ability between the hyperspectral data and soil nutrients. The result maps provide a spatially continuous surface mapping of the soil landscape, conforming to the pedological substratum. Finally, the hyperspectral remote sensing imagery can provide a new way for modeling and mapping soil fertility, as well as the ability to diagnose nutrient deficiencies.

Demand for agricultural products is increasing as population continues to grow in Africa. To attain a higher crop yield while preserving the environment, appropriate management of macronutrients (i.e., nitrogen (N), phosphorus (P) and potassium (K)) and crops are of critical prominence. This paper aims to review the state of art of the use of remote sensing in soil agricultural applications, especially in monitoring NPK availability for widely grown crops in Africa. In this study, we conducted a substantial literature review of the use of airborne imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing spectral information, and advances of these applications in farming practices by the African scientific community. Here we aimed to identify knowledge gaps in this field and challenges related to the acquisition, processing, and analysis of hyperspectral imagery for soil agriculture investigations. To do so, publications over the past 10 years (i.e., 2008–2021) in hyperspectral imaging technology and applications in monitoring macronutrients status for crops were reviewed. In this study, the imaging platforms and sensors, as well as the different methods of processing encountered across the literature, were investigated and their benefit for NPK assessment were highlighted. Furthermore, we identified and selected particular spectral regions, bands, or features that are most sensitive to describe NPK content (both in crop and soil) that allowed to characterize NPK. In this review, we proposed a hyperspectral data-based research protocol to quantify variability of NPK in soil and crop at the field scale for the sake of optimizing fertilizers application. We believe that this review will contribute promoting the adoption of hyperspectral technology (i.e., imaging and spectroscopy) for the optimization of soil NPK investigation, mapping, and monitoring in many African countries.

Located in a semi-arid to arid region, Morocco is confronting increasing water scarcity challenges. In the circular economy paradigm, the reuse of treated wastewater in agriculture is currently considered a possible solution to mitigate water shortage and pollution problems. In recent years, Morocco has made significative progress in urban wastewater treatment under the National Wastewater Program (PNA). However, rural sanitation has undergone significant delays. Therefore, an alternative technology for wastewater treatment and reuse in rural areas is investigated in this review, considering the region’s economic, social, and regulatory characteristics. Constructed wetlands (CWs) are a simple, sustainable, and cost-effective technology that has yet to be fully explored in Morocco. CWs, indeed, appear to be suitable for the treatment and reuse of wastewater in remote rural areas if they can produce effluent that meets the standards of agricultural irrigation. In this review, 29 studies covering 16 countries and different types of wastewater were collected and studied to assess the treatment efficiency of different types of CWs under different design and operational parameters, as well as their potential application in agricultural reuse. The results demonstrated that the removal efficiency of conventional contamination such as organic matter and suspended solids is generally high. CWs also demonstrated a remarkable capacity to remove heavy metals and emerging contaminants such as pharmaceuticals, care products, etc. The removal of microbial contamination, on the other hand, is challenging, and does not satisfy the standards all the time. However, it can be improved using hybrid constructed wetlands or by adding polishing treatment. In addition, several studies reported that CWs managed to produce effluent that met the requirements of wastewater reuse in agriculture of different countries or organisations including Morocco.