Abdellah El Aissaoui

Cereal–legume forage mixtures provide sustainable agronomic and environmental benefits for arid Mediterranean agricultural systems, but their productivity is often limited by within-field soil spatial variability. Site specific management practices are required to address the yield gaps. This study aimed to identify management zones in a no-till Triticale–Avena–Pea mixture by integrating apparent electrical conductivity (ECa) and NDVI measurements. ECa was mapped using an EM38-MK2 sensor, while NDVI was derived from UAV and Sentinel-2 imagery at four key growth stages. Plant biomass data and soil samples were collected for evaluating and explaining the yield gaps. Using K-means clustering, three management zones were delineated using ECa and NDVI data. Results showed a yield gap of 46% in fresh biomass between high- and low-productivity zones, associated with strong gradients in soil organic matter and total nitrogen availability. The delineation of management zones enabled targeted agronomic interventions, such as adjusting seeding rates in the high-productivity area and prioritizing soil improvement strategies in the low-productivity zone, thereby enhancing input-use efficiency.

Weeds create a significant challenge in agricultural production by competing with crops for essential resources such as nutrients, sunlight, and water. This competition leads to reduced crop yields and quality, resulting in substantial economic losses. Consequently, there is a critical need for effective weed control strategies to mitigate the impact of unwanted plant growth and ensure sustainable agricultural practices. In precision agriculture, enabling autonomous navigation between crop rows during tasks such as weeding and harvesting presents a significant research challenge, particularly when leveraging cost-effective technological solutions. Effective robot navigation requires adaptive traffic management strategies and robust object recognition capabilities to distinguish between cultivated and uncultivated areas. In fact, the integration of computer vision techniques into these systems is essential for optimizing trafficability in cropping fields and enhancing robots’ dynamics for better working efficiency in agricultural environments. This review addresses the challenge of enhancing inter-row navigation in field crops and delivering reliable guidance for autonomous agricultural robots. A PRISMA-based systematic review methodology was adopted to identify, screen, and analyze 38 relevant studies selected from the Scopus and Web of Science databases. The selected studies are classified according to their target platform (Unmanned Ground Vehicles and Unmanned Aerial Vehicles) and grouped into three methodological categories: conventional computer vision, deep learning architectures, and hybrid approaches. The findings provide practical guidance for selecting appropriate vision-based crop row detection technologies according to the application requirements and highlight key research directions toward more robust, cost-effective, and adaptable autonomous navigation systems. This article presents an outline of artificial-intelligence-based row detection methods used in agricultural fields and a classification of related semantic segmentation approaches. Unlike previous surveys, it provides an overview of the technological progress in agricultural robots and navigation based on systems vision for crop row detection, with a focus on comparisons balancing technical performance with economic and practical constraints.

Maize is a versatile crop that serves as a staple food for millions of people and provides various raw materials. Its adaptability to different climates and potential makes it economically valuable. However, the ongoing emissions of greenhouse gases pose significant challenges to sustain maize production. Sustainable agricultural practices are crucial to mitigate greenhouse gas emissions and reduce carbon footprints. Conservation tillage practices based on no-till promote carbon sequestration, and reduce carbon footprints compared to conventional tillage. These practices potentially improve soil health and water productivity. This chapter explores various aspects to sustain maize production, with a focus on conventional and conservation tillage systems, engineering technologies, carbon footprint reduction. It discusses also the challenges and perspectives in achieving sustainable maize production. It begins with an overview of conventional maize farming, highlighting its practices and challenges. The second section explores the advantages of conservation tillage in maize production. The third part focuses on engineering technologies and precision agriculture tools, as well as remote sensing. In the fourth section, strategies for reducing carbon emissions and adopting clean energy in maize farming are considered. The final part addresses the challenges and perspectives for sustaining maize production, discussing barriers, opportunities, and potential solutions.

This paper provides a new management method (MM) to optimally operate standalone direct current (DC) photovoltaic water pumping system (PVWPS) and synchronize its hydraulic performance to variable daily irradiance. The proposed approach well considers the issues from PV generator and a positive displacement pump mismatching to deserve a hydraulic network. Accordingly, Management Method uses small pumps mounted in parallel scheme to displace the hydraulic power point (HPP) in order to satisfy the mal adequacy between supply and load. Based on experimental Study; this approach showed a yielded standalone system, possibility of running pumping system using only photovoltaic supply (Without use of capacitance), and possibility to design more economical PV systems as the maximum peak power demand for each month can be satisfied using small power pumps in parallel and adjusting their switch with accordance to varying daily irradiance. Moreover, an analytical model was developed with reference to the obtained experimental results. This model based on a daily occurring irradiance as inputs in order to predict optimal performance outputs. It can be considered as a decision tool to track power point resulting from optimal electrical and hydraulic performances of the pumping system according to changes of the daily irradiance.

Nowadays, the world is encountering multiple challenges of energy security, economic recovery, and the effect of global warming. Investing in new fossil fuels only locks in uneconomic practices, sustains existing risks and increases the threats of climate change. In contrast, renewable energies, such as photovoltaic energy, constitute one of the most promising technologies in combating global increase in temperatures. Given its simplicity and low maintenance costs, photovoltaic energy is the most effective alternative to address the issues above. However, the standard test conditions (STCs) of PV modules are, in most cases, different from the real working conditions of a solar module. For instance, high levels of incident irradiation in an arid climate may cause the temperature of a module to rise by many degrees above the STC temperature of 25 °C, lowering the module’s performance. To effectively simulate and control PV systems for a given location, it has become paramount to develop a robust and accurate model that considers how PV modules behave. This study seeks to introduce an emerging metaheuristic optimization algorithm to estimate the unknown parameters of PV modules. The strategies deployed by flying foxes in the event of high temperatures have given birth to the development of a new metaheuristic algorithm called FFO. Contrary to previous methods, this new modeling procedure makes it possible to calculate all the parameters, regardless of temperature or irradiance. Four PV modules, having different technologies, were tested to evaluate the accuracy of the algorithm in question. The effectiveness of FFO is then contrasted with other well-known metaheuristics where single and double diode models are deployed. The results show that the FFO optimizer represents a substantial and compelling substitute for PV module extraction methods.

Biofortification is a promising and sustainable way to improve lentil nutritional value. No-till is an eco-friendly agricultural practice for sustainable agricultural production under climate change conditions. The objective of this study was to evaluate the genetic variation of lentil grain quality traits under no-till and conventional tillage systems, and to investigate the significance of tillage and genotype by tillage interaction. A Mediterranean lentil collection of 120 accessions, including landraces, advanced breeding lines, local varieties, and improved varieties was assessed for protein, iron, and zinc genetic variability under no-till and conventional tillage systems. Under no-till, substantial genetic variation for protein (19–32%), iron (17–184 mg/kg), and zinc (9–48 mg/kg) was observed, indicating the possibility of lentil biofortification under conservation agriculture. Significant effects of tillage system and genotype by tillage interaction were observed for protein and iron contents. Furthermore, significant effect of tillage system on zinc content was recorded. No-till yielded slightly higher protein and zinc content than conventional tillage. Overall, the results indicated that shifting lentil production from a conventional tillage system to a no-till system would be of interest to increase lentil nutritional value.