This comprehensive review article illuminates Morocco's proactive response to climate change challenges over the past decades. Faced with drought episodes akin to other Mediterranean countries, Morocco contends with significant water stress, boasting limited water resources estimated at 20 billion cubic meters and an annual per capita consumption of 700 m3. The country experiences a temperature surge of 0.3–2.5 °C, coupled with decreased precipitation, heightening the impact of climate change on water resources. Morocco plans a 40 % reduction in emissions by 2030 (18.3 % unconditional, 27.2 % conditional on international aid) and an impressive 77 % decrease in emissions by 2050 compared to Business as Usual levels. The study offers a comprehensive, groundbreaking analysis, representing the first comprehensive review of Morocco's low-carbon strategy and energy initiatives. It meticulously examines 155 research papers through the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, categorizing solutions into engineering and managerial domains. Covering power, transport, building, industry, and agriculture, the article provides a taxonomy of engineering solutions, including renewable energy, energy auditing, efficiency, sufficiency, and digitalization of management. Simultaneously, managerial solutions, ranging from education to subsidies and policy reforms, are detailed. These solutions contribute to Morocco's standing as a leader in climate action and clean energy transition. Despite these significant contributions, the article acknowledges potential limitations, including reliance on available literature and regional variations in solution applicability. Nonetheless, it serves as a foundational resource for understanding Morocco's endeavors in addressing climate change and promoting sustainable energy practices. 

Long-term low carbon strategy of Morocco: A review of future scenarios and energy measures

This article explores the transformative potential of artificial intelligence (AI) tools across the agricultural value chain, highlighting their applications, benefits, challenges, and future prospects. With global food demand projected to increase by 70% by 2050, AI technologies—including machine learning, big data analytics, and the Internet of things (IoT)—offer critical solutions for enhancing agricultural productivity, sustainability, and resource efficiency. The study provides a comprehensive review of AI applications at multiple stages of the agricultural value chain, including land use planning, crop selection, resource management, disease detection, yield prediction, and market integration. It also discusses the significant challenges to AI adoption, such as data accessibility, technological infrastructure, and the need for specialized skills. By examining case studies and empirical evidence, the article demonstrates how AI-driven solutions can optimize decision-making and operational efficiency in agriculture. The findings underscore AI’s pivotal role in addressing global agricultural challenges, with implications for farmers, agribusinesses, policymakers, and researchers. This article aims to advance the evolving research and discussions on sustainable agriculture, contributing insights that promote the adoption of AI technologies and influence the future of farming. 

Artificial Intelligence Tools for the Agriculture Value Chain: Status and Prospects

The management of water resources is becoming increasingly important in several contexts, including agriculture. Recently, innovative agricultural practices, advanced sensors, and Internet of Things (IoT) devices have made it possible to improve the efficiency of water use. However, it is the application of control strategies based on advanced machine learning techniques that enables the adoption of smart irrigation scheduling and the immediate economic, social, and environmental benefits. This challenging research area has attracted the attention of many researchers worldwide, who have proposed several technological and methodological solutions. Unfortunately, the results of these scientific efforts have not yet been categorized in a thematic survey, making it difficult to understand how far we are from optimal water management based on machine learning. This paper fills this gap by focusing on smart irrigation systems with an emphasis on machine learning. More specifically, the generic structure of a smart agriculture system is presented, and existing machine learning strategies and available datasets are discussed. Furthermore, several open issues are identified, especially in the processing of long-term data, also due to the lack of corresponding annotated datasets. Finally, some interesting future research directions to be pursued in order to build scalable, domain-independent approaches are proposed.

Machine Learning for Smart Irrigation in Agriculture: How Far along Are We?

We present a new look at the classification of real low-dimensional Lie algebras based on the notion of a linear bundle of Lie algebras. Belonging to a suitable family of Lie bundles entails the compatibility of the Lie–Poisson structures with the dual spaces of those algebras. This gives compatibility of bi-Hamiltonian structure on the space of upper triangular matrices and with a bundle at the algebra level. We will show that all three-dimensional Lie algebras belong to two of these families and four-dimensional Lie algebras can be divided in three of these families.

Linear Bundle of Lie Algebras Applied to the Classification of Real Lie Algebras

In comparison to machine learning algorithms, deep learning algorithms are more effective in terms of their ability to make accurate predictions about agricultural data. When trying to attain better outcomes, the accuracy and efficiency of the algorithm are highly crucial factors to consider. In order to help the farmers to accurately predict the yields of their crops, the study of agricultural data is of great assistance. This resulted in a significant amount of study, and the researchers used a variety of data to make predictions on the yields of the various crops. The primary objective of this study is to locate a more effective deep learning algorithm that may be used to forecast crop production. Hence the SLR was carried out and finally identified 20 opted research articles related to deep learning models utilized in agricultural data prediction.  As a result, this SLR discovered that the LSTM-based deep learning models provide more accurate predictions when applied to agricultural data sets. A great number of research papers made use of the same model for better results.

A Comprehensive Analysis of Deep Learning Models in Agricultural Crop Yield Prediction

The conventional sprinkler irrigation time calculation does not consider current soil moisture, leading to inadequate or excess irrigation due to daily weather changes. In present study, a new approach for calculating sprinkler irrigation time was proposed, which used in-field soil moisture values obtained by a convolutional neural network (CNN). To enhance accuracy and limit its size, the CNN architecture incorporated depth-wise separable convolution and residual connections. A deep-learning-based mobile application was developed, which predicted soil moisture class via in-field soil images, crop factors, and sprinkler system details. The system was assessed based on soil moisture class prediction accuracy, energy usage, water savings, and water productivity. The CNN model had an average classification accuracy of 97.10%, precision of 85.50%, recall of 86.80%, F1-score of 85.80%, and true prediction score of 75.30%. The developed mobile application saved 27.59% water and 27.42% energy by accurately estimating irrigation time using the predicted soil moisture class. The developed system corrected conventional irrigation depth in some instances, thereby reducing crop yield losses due to inadequate water supply. Compared to conventional sprinkler irrigation, the developed system increased water productivity by 32.75%. Thus, the system has the potential to conserve water and energy while improving crop yield. 

Optimization of irrigation timing for sprinkler irrigation system using convolutional neural network-based mobile application for sustainable agriculture

The weather, as well as the difficulty of forecasting it, has contributed to the fall in crop output. As a result, farmers rely on accurate weather forecasts to run their businesses. Agriculture is interwoven with other industries, including the sugar industry, which relies on sugarcane farmers. They may be able to better organise their logistics by employing machine learning or artificial intelligence to estimate agricultural production. As a result, both AI and connected IoT devices have a lot of potential in agriculture. When estimating agricultural yields, there are various elements to consider. Weather trends, satellite images, soil quality, and the potential of an infestation are all investigated in this location. When all of these criteria are taken into account, a complete picture of when crops should be planted emerges. In the event that unanticipated problems develop, there are also “what-if” scenarios and backup plans in place. Climate change can affect agricultural production in a variety of ways, including changes in average temperatures, precipitation amounts, heat waves, CO2 levels in the air, and ozone levels at ground level. Climate change has a wide range of affects from country to country. Lower-elevation countries are more prone to have difficulties producing crops. Equipment capable of accurately predicting the evolution of weather patterns is required Weather forecasts based on statistics look at previous data to see if there are any patterns between seasons. As a result, the winter data can be used to forecast summer temperatures. Winter temperatures will almost probably alter if summer temperatures change. In the published literature, researchers have detailed their confirmed solutions to various problems. If prior concepts are used without first receiving a qualified review, future researchers will not get the desired results. If the researchers don't first undertake a thorough investigation of the most recent indications of performance, it's likely that they won't be able to find evidence of future growth in the direction of excellence. Students must perform significant investigation on a variety of concepts and their connections to modern advancement in order to become proficient researchers. In order to meet these demands, this paper covers the publication of multiple papers in various journals at various times over the years.

Review on Crop Yield Prediction with Deep Learning and Machine Learning Algorithms

The existence of a class of smooth Lie algebra bundles is proved and examples are given. Further we construct smooth vector bundles which have the structure of non trivial smooth Lie algebra bundles.

On smooth lie algebra bundles

We define the notion of Lie Ehresmann connection on Lie algebra bundles and show that a Lie connection on a Lie algebra bundle induces a Lie Ehresmann connection. The converse is proved for normed Lie algebra bundles. We then show that the connection on adjoint bundle corresponding to the connection on principal G—bundle to which it is associated is a Lie Ehresmann connection. Further it is shown that the Lie Ehresmann connection on adjoint bundle induced by a universal G-connection is universal over the family of adjoint bundles associated to G-bundles.

Connections on smooth Lie algebra bundles

We study the pullback Lie algebra (group) bundle of a Lie algebra (group) bundle and show that the Lie algebra bundle of the pullback of a Lie group bundle $mathfrak{G}$ is isomorphic to the pullback of the Lie algebra bundle of $mathfrak{G}$. Then, using the notion of Lie connection on a Lie algebra bundle, we show that the pullbacks of a Lie algebra bundle $xi$ over a smooth manifold $M$ with respect to two smooth homotopic functions $f_0 , f_1 : N rightarrow M$ are isomorphic to Lie algebra bundles over $N$.

https://jart.guilan.ac.ir/article_4191_3712eb10e7125bda991a9f59b3684336.pdf

Pullback of lie algebra and lie group bundles, and their homotopy invariance

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