Under sandy soil conditions, increasing the efficiency of potassium (K) fertilizers is considered to be a major limiting factor for improving the productivity and quality of fruit crops. In this context, utilizing nanotechnology has emerged as a novel technique to increase the efficiency of K applications. In our study, two field trials were conducted, in two consecutive seasons (2019/2020 and 2020/2021), to compare the effects of nano-chitosan loaded with K as a foliar treatment with those of conventional soil applications of K on plant growth, yield, and quality of strawberry plants grown in sandy soil. Strawberry plants were treated with 12 different treatments, which were replicated three times in a randomized complete block design in each growing season. Potassium sulfate (K2SO4, 48% K2O) was applied to the soil at a rate of 150.0 kg acre?1 (recommended rate, 100%). Meanwhile, the spraying of nano-chitosan loaded with K was applied at 1000 mg L?1 as a control. In addition, K2SO4 was applied either individually or in combination at the rate of 112.5 or 75.0 kg acre?1 with four nano-chitosan-K dosages (250, 500, 750, and 1000 mg L?1). After harvesting, soil samples were collected and prepared to determine K fractions. As well, plant samples were collected to determine the vegetative growth parameters and the foliage content of NPK and chlorophyll. Eventually, the yield traits and quality parameters were evaluated. A principal component analysis was conducted to determine the interrelationships of the treatments’ averages and their effects on yield components and quality traits. A combined analysis was performed for the two studied seasons and the values were the mean of six replications. The results indicated that the application of common K fertilizer (150.0 kg K2SO4 acre?1) resulted in the maximum increase in soluble and exchangeable K in the soil, which was comparable to those observed with 112.5 kg K2SO4 acre?1 + 1000 mg L?1 nano-chitosan-K and 112.5 K2SO4 acre?1 + 750 mg L?1 nano-chitosan-K. The total yield, marketable yield, and fruit firmness were all significantly increased by the latter two treatments compared to the control group. Furthermore, plots treated with 112.5 kg K2SO4 acre?1 + 1000 mg L?1 nano-chitosan-K significantly increased the total soluble solids, vitamin C levels, acidity, total sugar, and anthocyanin levels in strawberry fruits. In conclusion, under sandy soil conditions, the utilization of nanoparticles could be an indispensable tool for manipulating fertilization management when cultivating strawberries. The K status of the soil was improved by applying 75% of the recommended dose of mineral K in combination with 1000 or 750 mg L?1 of nano-chitosan-K, without compromising strawberry yield or quality.

Horizontal agricultural expansion is one of the most important pillars of
the national economy in Egypt, so the country makes strenuous efforts
to make optimal use of agricultural resources, and despite these efforts,
the climate prevents it
.Recently, global warming has increased and
climate change is expected to worsen the frequency, intensity, and
impacts of some types of extreme weather events that affected mango
productivity during the last years, which led to a decrease in
productivity by about 37%, with a value of financial losses amounting
to 4.8 billion pounds in 2021.Therefore, the research aims to estimate
the impact of extreme weather on mango productivity during the last
three years (2021-2019) at four studied governorates representing 75%
of the total area of mango in Egypt. The Fixed Effect Cross of Panel
Data Model was adopted as the appropriate model, to illustrate the
impact of the phenomena on the productivity of mangoes. The results
show that there is a statistical significance and a negative effect for both
the minimum and maximum temperatures, as productivity decreases by
about 1.05%, and 1.79% with an increase in each of the minimum and
maximum temperatures by 1%, respectively. The negative impact has
been shown on the governorates of Buhaira and Ismailia. Also, the
individual effect was studied for each month,it was found that there was
a statistically significant negative effect of the minimum and maximum
temperatures for the months of flowering and fruiting.

The blister beetles Meloe proscarabaeus Linnaeus, 1758 (Coleoptera: Meloidae), is a
dangerous pest that threatens the agriculture of faba bean fields in El-Farafra Oasis,
New Valley Governorate. In this study, an evaluation of the efficiency of different
pit-fall trap colors for capturing adults of the blister beetles has been performed in
faba bean (Vicia faba L.) fields. The experiment revealed that the green and red
traps showed the highest number of captured beetles during the 2020 and 2021
seasons, which was highly significant to other traps’ colors. On the other hand,
black, blue, gray, white, and yellow traps showed insignificant differences in the
number of captured beetles. Concerning the sex of trapped beetles; it could be
highlighted that the green trap attracted more female beetles than males with
significant differences. Inversely, the red color trap attracted more males than
females with significant differences. Approximately 40% of the captured beetle
population was recorded in March, while only 11% were trapped in April. A Green
pit-fall trap could be deemed a new estimating assay to suppress M. proscarabaeus
adults in faba bean fields since the color trap variation affected the number of
captured beetles. Therefore, color traps can be relied upon as an effective method
in controlling beetles without the number of beetles reaching the limit of economic
damage and in a manner that is safe for the environment.

Two field experiments were conducted during the two successive winter seasons of 2020-2021 and 2021-
2022 on the experimental farm belonging to the Central Laboratory for Agricultural Climate, Agricultural
Research Center, Giza Governorate, and Egypt. The study comprised three irrigation levels (50, 75, and 100%
of estimated water requirement based on climatic data) and four soil cover treatments namely transparent
polyethylene mulch (PE), rice straw (RS), date palm fiber wastes (DPf) and control (bare soil). Hot pepper
seeds (Capsicum annuum L.), Super Noura F1 hybrid. Hot pepper transplants were cultivated in the field in
the first week of September for both seasons. The main results show that using 100% water level led to
increased vegetative and yield of hot pepper during both seasons followed by using 75% while deficit
irrigation gave the lowest hot pepper growth and yield. Using 75% water level gave the highest water use
efficiency. Using PE mulch led to increasing the soil temperature during the growth season followed by rice
straw mulch whereas the date palm fiber wastes mulch decrease soil temperature during both seasons.
Moreover, PE treatment led to an increase in the growth and yield of hot pepper during both seasons. Control
treatment combined with a 50% water level decreased the growth and productivity of hot pepper during the
two seasons.

High agricultural production is essential for food security. Hence, it is important to increase crop yields while minimizing losses. Insects cause considerable economic losses.Entomopathogenic Nematodes (EPNs) are alternatives to chemical pesticides for controlling insects. For researchers, determining the type of EPNs is not an easy task. Thus, a tool for identifying EPNs species is needed. In this paper, we introduce a method for developing a system to identify EPNs species according to their morphometric traits. We used Web Ontology Language (OWL) to build the ontology of EPNs species and represent their semantic information. Ontology helps in data representation, exchange, and interoperability. The proposed system was implemented as a mobile application that extracts and retrieves EPNs data from ontology. It displays the details of valid Heterorhabditis and Steinernema species.
Also, it enables us to find species that are related to the given infective juveniles (IJs) features. We used techniques of similarity search such as cosine similarity and Euclidean distance to compare different EPNs species and identify similar species based on appearance features. The results indicate that the system can recognize the known EPNs species and it helps to identify similar species.

Wheat is one of the most important crops in the world and was considered the major grain crop grown in Egypt. Nowadays, Egypt is the largest wheat importer in the world and consumes an extensive amount of it. To decrease the gap between production and consumption and increase the yield, we need to control wheat diseases, especially stripe rust, due to its major damage to wheat. Further, we need to advise farmers as early as we can to control and treat them. The paper proposed an interactive intelligent system to monitor, predict and give the correct advice at the right time to farmers. This system is called MISSR (Mentoring Interactive System for Stripe Rust). The system is considered an important means to effectively prevent risks in agricultural production. It also plays an important role in guiding farmers and decision-makers to plan and implement suitable practices to increase yield and mitigate stripe-rust disease. On the other hand, it can acquire relevant and timely information in the areas where this information or data is unavailable. To build this model for the wheat crop in Egypt, we used wheat experts’ knowledge and climate data API. MISSR is available as a mobile application to provide access for more farmers and increase its availability.

Date palm (Phoenix dactylifera L.) is related to the family Arecaceae, which is considered one of the most ancient economically cultivated crops. Mainly, it is grown in the arid regions of the Middle East and North Africa. The crucial matter in maintaining the diverse number of date palm cultivars in Egypt is its biodiversity conservation of it. In order to progress programs and cultivar characterization and conservation to combat genetic erosion, we must estimate the genetic variability and right date palm cultivar identification this is the important point to present a comprehensive investigation for Egyptian date palm genome variations and develop novel DNA markers (SNPs and indels) in four date palm cultivars using SOLiD sequencing.

Neurexin1 gene is essential for formulating synaptic cell adhesion to establish synapses. In a previous work, 38 SNPs in Neurexin1 recoded in mental disorder patients have been collected. Five computational prediction tools have been used to predict the effect of SNPs on protein function and stability. Only four SNPs in Neurexin1? have deleterious prediction results from at least four tools. The current work aims to use molecular dynamic simulation (MD) to study the effects of the four mutations on Neurexin1? both on the whole protein as well as identifying affected domains by mutations. A protein model that consists of five domains out of six domains in the real protein was used; missing residues were added, and model was tested for quality. The MD experiment has last for 1.5 ?s where four parameters have been used for studying the whole protein in addition to three more parameters for the domain analysis. The whole protein study has shown that two mutations E427I for Autism and R525C for non-syndromic intellectual disability (NSID) have distinctive behavior across the four used parameters. Domain study has confirmed the previous results where the five domains of R525C have acted differently from wild type (WT), while E427I has acted differently for four domains from wild type. The other two mutations D104H and G379E have three domains that only acted differently from wild type. The fourth domain of all mutations has an obvious distinctive behavior from wild type. Further study of E427I and R525C mutations can lead to better understanding of autism and NSID.

The Early Warning System (EWS) is a critical tool for efficiently
preventing hazards in agricultural productivity, as well as pests and
illnesses. Early detection of plant diseases helps in increasing crop
yields and decreasing losses. EWS can acquire relevant and timely
information in areas where this information or data is unavailable.
This paper presents a framework aimed to warning farmers of the
expected crop diseases that might affect their crops. It will support
a timely recommendation for the appropriate agriculture practices
directed towards correct farm management. The proposed
framework objective is to design a model for utilizing weather
forecasting and domain knowledge that is related to the effect of
weather on plant diseases. The framework output depends on the
integration of weather data, which might affect crop diseases and
farmers’ databases that include farmers’ locations and cultivated
crops. Furthermore, it will enable agriculture extension agents to
communicate with farmers and provide them with advices about
weather data and how to deal with it to preserve crops and increase
yields.

The biggest threat to the safety of food is plant diseases. They have the ability to dramatically lower the quantity and quality of agricultural products. Recognizing plant diseases is the biggest issue in the agricultural industries. Convolutional Neural Networks (CNN) are effective in solving image classification problems in computer vision. Numerous deep learning architectures have been used to diagnose plant diseases. This study presents a transfer learning-based model for identifying diseases in plant leaves. In this paper, a CNN classifier based on transfer learning model called DenseNet201 are proposed. An analysis of four deep learning models (VGG16, Inception V3, ResNet152V2, and DenseNet201) done to see which one can detect plant diseases with the greatest degree of accuracy. Web based application developed for plant disease diagnosing from defected leaf image and the proposed model which identify the disease and give the recommended treatment. The used images dataset contains 28310 leaves photos of 3 crops, tomato, potato and pepper divided into 15 different classes, 9 disorders and one healthy class for tomato, 2 disorders and one healthy class for potato and 1 disorder and one healthy for pepper. In our experimental, the results shows that the proposed model achieves the highest training accuracy of 99.44% and validation accuracy of 98.70%.