An innovative gene editing system, clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 technology, has significantly revolutionized agriculture by improving the quality of crops and sustaining environment. It has become the most powerful tool for crop improvement due to its flexibility. CRISPR adapted the natural bacterial and archaea defense mechanism against invading viruses or other foreign DNA into a genome engineering tool that has remarkable crop breeding progress by virtue of its precision in specific gene editing. This study outlines the current application of CRISPR/Cas9 technology in crop yield, quality, disease resistance and environmental stress. In plants, CRISPR/Cas9-based gene-editing consists of the selection of specific target sites, designing and synthesis of single guide (sgRNA), delivery of transformation carrier or ribonucleoprotein (RNP) in plant cells, transformation, and regeneration of gene-edited plants. At present, the plant CRISPR/Cas9 and its derived system have shown several genome-editing activity, like gene editing, homology-directed repair (HDR), and transient gene silencing or transcriptional repression as well. Furthermore, simultaneous editing on multiple genes has contributed to pathway-level plant biotechnology researches that widely expand genome engineering of agronomic traits, and its adoptability. In addition, the challenge in the future application is also discussed. The CRISPR genome editing technology is not only equivalent to traditional breeding technique but actually much more controlled and faster.

Sweet cherry tree is one of the most important crops worldwide, producing fruits with high economic importance due to its nutritional value and bioactive properties, with benefits to human health. Due to the currently unstable climatic conditions, cherry cracking has become a significant disorder, strongly affecting the quality and yield of cherry orchards. A cracking rate of 20-25% at harvest can render cherry production unprofitable, decreasing the commercial value of the fruit, as only the cracked ones can be sold to processing industries. This study aims to assess the impact of calcium and seaweed-based biostimulant applications on sweet cherry quality and profitability in cv. Sweetheart. Applying 300 g hL^-1 of calcium led to a significant 52% reduction in the cracking index and a substantial 136% increase in orchard yield. Similarly, applying 150 mL hL^-1 of seaweed resulted in a 2% increase in fruit weight and a 3% decrease in the cracking index. Therefore, our findings suggest that calcium and seaweed-based biostimulant could serve as novel and sustainable alternatives for orchard producers, enhancing cherry profitability and marketability.

Sustainable and efficient agricultural production is a growing priority in modern society. Viticulture, an important agricultural and food sector, also faces this challenge. Precision Viticulture (PV) has gained prominence as it aims to foster high-quality, efficient, and environmentally sustainable practices. The Soluble Solids Content (SSC) is essential for assessing grape ripeness and quality in the winemaking process. Conventional methods for determining SSC values (expressed in ºBrix) are invasive, expensive and labour-intensive, necessitating sample preparation, making large-scale analysis impractical. In response to these limitations, this study presents an innovative approach within the field of Precision Viticulture. It focuses on the non-invasive prediction of SSC using low-cost Proximal Hyperspectral Optical Sensors. These sensors rely on spectral reflectance measurements in the range of 340-850 nm. The study was conducted in a commercial vineyard in the Demarcated Douro Region, Cima-Corgo sub-region, Portugal, over six weeks during ripening. 169 grape berries from Touriga Nacional vines were analyzed under three irrigation regimes (no irrigation, 30% ETc, and 60% ETc). After organizing and preprocessing the data, machine learning algorithms, namely Partial Least Squares Regression (PLS), Random Forest (RF), and Generalized Linear Model (GLM), were applied to predict SSC values. These models' performance was thoroughly evaluated using cross-validation techniques. The performance of different models was evaluated showing significant differences, according to the metrics used (R2, RMSE and MAPE). The RF model demonstrated effectiveness and precision. A high R² value of 0.9312, coupled with low RMSE (0.9199 ºBrix) and MAPE (3.88%), signifies a strong fit to the data and accurate predictive capabilities. The results of this benchmarking study on predictive models of SSC provide valuable insights into the performance of various models, aiding winegrowers and winemakers in decision-making.

In recent years, Tunisia has experienced severe drought, highlighting the need for careful water resource management to ensure both sufficient water availability and long-term sustainability. The use of field data to determine the water use of cultivated crops is a challenging task, with uncertainties due to the lack of representativeness of measurements. The use of remote sensing tools appears as a promising solution.

The study region of our work is located in the south-west of Tunisia, at Kairwan (latitudes 35° to 35° 45’ N and longitudes 9° 30’ to 10° 15’ E). The region is characterized by its arid climate and the overexploitation of the groundwater. We consider three different soil coverage types namely: cereals (LC1), fallow (LC2) and bare soil (LC3). Data used (nine Sentinel-1 and 12 Sentinel-2 images) are downloaded from the Copernicus platform. This paper proposes the following contributions: (1) a formula called 'ER' to estimate soil water status from Sentinel-1 data, (2) two color composition images to monitor changes in soil moisture and crop cover, and (3) the monitoring of the impact of climate change on land use through unsupervised classification using ISODATA and K-Means.

The results demonstrate the importance of the synergy between optical and radar satellite data in determining the soil water status via remote sensing.

Salinization is an increasing problem worldwide, limiting crop production. Soil salinity causes ion toxicity, osmotic stress, nutrient deficiency and oxidative stress on plants, leading to the overproduction of reactive oxygen species (ROS). To counterbalance these effects, plants activate a complex detoxification system through the action of antioxidant pigments, carotenoids, phenolics and flavonoids, and the accumulation of minerals, that play an important role in human health against several diseases.

In this study, we investigated the impacts of salinity (0, 50, 100, 300 mM NaCl) on the flowers of three Tagetes patula cultivars harvested after 14 days, recording total carotenoids, minerals, carotenoids, ascorbic acid, total polyphenol content, and total flavonoid content. Results showed an overall increase in all compounds with the increase in salinity levels, in comparison with control conditions. Nevertheless, salinity (most especially 100 and 300 mM) strongly affected plant size and flower production.

Results showed that edible marigold flowers are a promising crop with enriched nutritional contents and antioxidant activity that can be a new source of source of nutraceuticals. However, this study also reports, for the first time, the harsh effects of high salinity in the production of flowers, limiting its production in high-salinity soils. We recommend its production in a short exposure to salinity up to 100mM NaCl to achieve a high production of nutraceuticals without compromising the viability of flower production.

Urban agriculture has evolved as a cornerstone of sustainable development, acting as a "magic wand" to address challenges related to food, energy, and environmental sustainability. With a growing need for innovative agricultural solutions, Morocco firmly aligns its policies with international environmental agreements and implements programs aimed at improving the sustainable use of water resources and promoting sustainable agriculture. This is particularly essential as agriculture accounts for approximately 84% of water demand.

Similarly, since its establishment as a garden city in 1062, Marrakech has benefited from a sophisticated irrigation system (Khettaras, etc.) supporting agriculture. However, the city has recently faced significant challenges, both environmentally (climate change, water scarcity, etc.) and socio-economically (population growth, food insecurity, socio-economic inequalities, etc.).

This research aims to shed light on the current situation of urban agriculture in Marrakech and identify the constraints threatening its agricultural viability. Additionally, it aims to study several urban agriculture models to determine the best flexible and sustainable strategy to address socio-economic and environmental issues in the specific context of Marrakech. Through an in-depth investigation of the potential benefits and limitations of these models, the research aims to promote sustainable agricultural practices in the city, thereby ensuring rational, intelligent, and sustainable use of urban agriculture's potential. Ultimately, this research aims to increase agricultural resilience in Marrakech and contribute to its long-term sustainability.

Bio-control agents are microbiological-based alternatives to agrochemicals due to their sustainable attributes in controlling phytopathogens. This research highlights the isolation and formulation of bio-control agents using sugarcane bagasse as a carrier matrix. The isolated rhizospheric organisms were screened for antibiosis traits using the agar streak method. Bacterial isolates that showed positive traits were further identified phenotypically. Preparation of the carrier was done by oven drying the sugarcane bagasse at 90⁰C for 3 days, ground and sieved using a 1.16 mm sieve. For the bio-control formulation, 200ml of biocontrol inoculum was added to 20g of sugar cane bagasse for each organism to form the final products. Water and adhesion capacities were conducted on the three formulations and afterwards the in vivo antagonistic potential of the formulants was assessed on maize cultivation for 21 days. A total of 9 isolates were obtained, only three (3) showed antagonistic activity for antibiosis trait which were used for the formulation branded as ZEEMYC (Mycobacterium spp), ZEEPAS (Pseudomonas spp), and ZEEBAC (Bacillus spp), respectively. The water capacity of the three formulations were between 6.9g - 9.9g, respectively while adhesion capacity was also displayed. At day 5, maize seeds in all pots sprouted except diseased seeds without bio-control (DS). At day 11, plant height, shoot length and root length ranged between (36.5cm-39cm), (31.cm-34cm), (5cm-6cm) for plants with biocontrol agent, control was 42cm, 34.5cm, 7.5cm while barely visible growth was observed in DS. This study displays the potential of natural-based biocontrol agent in controlling phytopathogen Aspergillus niger.

With the rising impact of climate change on agriculture, insect-borne diseases are proliferating. There’s a need to monitor the appearance of new vectors to take preventive actions that allow to reduce the use of chemical pesticides and treatment costs. Thus, agriculture requires advanced monitoring tools for early pests and disease detection. This work presents a new concept design for a scalable, interoperable and cost-effective smart trap that can digitize daily images of crop-damaging insects and send them to the cloud server. However, this procedure can consume approximately twenty megabytes of data per day, which can increase network infrastructure costs and require a large bandwidth. Thus, a two-stage system is also proposed to locally detect and count insects. In the first stage, a lightweight approach based on the SVM model and a visual descriptor is used to classify and detect all regions of interest (ROI) in the images, which contain the insects. Instead of the full image, only the ROI are then sent to a second stage on the pest monitoring system, where they will be classified. This approach can reduce, by almost 99%, the amount of data sent to the cloud server. Additionally, the classifier will identify unclassified insects in each ROI, which can be sent to the cloud for further training. This approach reduces internet bandwidth usage and helps to identify unclassified insects and new threats. In addition, the classifier can be trained with supervised data on the cloud and then sent to each smart trap. The proposed approach is a promising new method for early pests and disease detection.

Sensing devices (eg multispectral) coupled with advanced analysis methods (eg AI) autonomously collect and process in-situ phenotype data (ie observable plant traits resulting from the performance of a genotype in a specific environment). However, this approach faces limitations, namely the low integration of phenotype data in decision support systems for leveraging agricultural practices and predicting plant behaviour amid complex genotype, environment and management interactions (GEM). To enhance the role of digital phenotyping in supporting Precision Agriculture, this paper proposes a sensing network based on IoT. The developed system comprises three modules: data collection–stationary and robotics-assisted sensors to gather phenotype data; communication–physical data connections between devices and virtual connections over the internet transferring the data to a centralised database–designed to receive, store, and process data. In the database, several processes occur simultaneously, namely data visualisation to confirm the correct sensors and data stream functioning. Also, phenotype data will be merged with a crop growth model (CGM), decreasing the simulation uncertainty and obtaining, in advance, insights about plant behaviour considering GEM conditions. To assess the performance of the proposed network, a greenhouse was equipped with several sensors that collect plant, environment and soil data (eg leaves number, air temperature, soil moisture). Lettuce plants were induced to nitrogen stress to characterise physiologic plant shifts and evaluate the reliability of CGM predictions. The proposed network can provide real-time-causal support toward advanced agricultural practices, evolving from a data-driven approach to an integrative framework where context (GEM) drives advanced decision-making.

Pollination is a crucial biological process that underpins crop yield and quality, as well as sustains other ecosystem services essential for our planet’s life. Insects are the largest group of pollinators, particularly bees, handling the pollination of 71 of the 100 crops that contribute to 90% of the world's food supply. Nevertheless, both biotic and abiotic factors exert considerable influence on bee behaviour, which in turn affects the pollination process. Moreover, the alarming decline in bee populations, along with other essential insect pollinators, presents a significant challenge to natural pollination. This work focuses on Actinidia, a dioecious plant, i.e., with female and male flowers on separate plants, which introduces entropy into the pollination phase. In this plant, the number of pollinated seeds directly influences the size of Actinidia fruits, so the success of the pollination phase is fundamental. However, natural pollination in Actinidia is mainly entomophilic, i.e., by insects. Hence, the exploration of alternative approaches becomes essential. To address this need, there has been a growing interest in robotic solutions for pollination, these solutions present various tools to perform pollination. This work aims to study the available technologies to perform artificial pollination processes. A study of the different options in the literature is performed, analysing the advantages and disadvantages of each method to support the artificial pollination process.