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.

Water deficit in arid and semiarid regions limits eggplant (Solanum melongena L.) productivity and
growth indicators. In this work, an experiment was conducted during two studied seasons of 2020 and
2021 on the farm belonging to the Central Laboratory for Agricultural Climate, Dokki, Giza
governorate, Egypt. This work aimed to study the effect of different irrigation levels and nitrogen forms
on the growth and yield of eggplant (Solanum melongena L., c.v. classic). Three irrigation levels, i.e.,
50, 75, and 100% of irrigation requirements (IR), and three nitrogen forms, i.e., ammonium sulfate
(NH4 ) 2 SO4 , calcium nitrate Ca(NO3 ) 2 and ammonium nitrate NH4 NO3 were applied in a split-plot design
with three replicates. Potential evapotranspiration (ETo ) was calculated using the Penman Monteith
equation and then irrigation requirements for different irrigation rates were estimated. The obtained
results showed that the highest vegetative growth, i.e., number of leaves, plant height, and total fresh
weight were obtained by applying irrigation level 100% of IR, but the stem diameter and total dry
weight were recorded by using irrigation level 75% of IR. The irrigation level 75% of IR significantly
increased total and early yield during the two successive seasons. Regarding nitrogen form treatments,
the highest vegetative growth was obtained by applying ammonium nitrate to the soil, followed by
calcium nitrate. The interaction effect between irrigation levels and nitrogen forms was clear with the
100% irrigation level combined with ammonium nitrate giving the highest vegetative growth. Eggplant
yield took another trend, the highest yield was obtained by using ammonium nitrate fertilizer under the
irrigation level 75% of IR. Water use efficiency (WUE) had the same trend, applying 75% of IR gave
the highest WUE values. Application of ammonium nitrate fertilizer led to increasing WUE compared
to the other treatments during the two studied seasons.

The purpose of the present study is to determine the effect of wheat yellow rust caused by Puccinia striiformis f.sp. tritici on quantitative and qualitative grain yield losses, in addition to predict them through regression model for each tested wheat variety, i.e., Shandweel-1, Gemmeiza-11, Sids-1, Sids-12, Sakha-94 and check variety Morocco in 2020 and 2021seasons at Bahteem Agricultural Research Station, Agricultural Research Center, Egypt.
The disease parameters i.e., final rust severity (FRS %), rate of yellow rust increase (r-value) and area under disease progression curve (AUDPC) in the first season were higher than the second season. Gemmeiza-11 and Sids-12 varieties recorded the highest values of these parameters during the two seasons compared to the check variety Morocco. Corresponding data ranged from 63.33 to 83.33% for FRS (%) 0.043 to 0.063 for r-value and 783.33 to 1350.0 for AUDPC. Shandweel-1 and Sids-1 varieties recorded the moderately values of these parameters but
Sakha-94 recorded the lowest values of the same parameters. The actual losses (%) values, ranged from 1.31 to 36.44% for 1000 kernel weight/gm and grain yield/plot (kg), were lower than the total losses (%) which ranged from 1.38 to 42.94%, respectively, for each of wheat varieties in the two seasons under study. The highly susceptible wheat varieties, Sids-12 and Gemmeiza-11 exhibited the highest values of actual losses (%) and total losses (%) up to 42.94%, while the moderately susceptible varieties, Shandweel-1 and Sids-1 were less, being
21.05% compared to the check variety Morocco. In addition, Sakha-94 variety showed less than 3.37%. The regression analysis showed that a positive strong correlation between FRS and total losses (%) of 1000 kernel weight/gm and grain yield/plot (kg), during the two growing seasons. The qualitative properties of wheat varieties revealed that the reduction rates for carbohydrate mg/g, total protein mg/g, crude fiber, dry gluten and wet gluten were higher in the susceptible wheat varieties, Shandweel-1, Gemmeiza-11, Sids-12 and Morocco (check), where the corresponding values ranged from 3.80 to 33.07%, while Sakha-94 and Sids-1 showed the lowest reduction from 1.30 to 9.6%, in the tested chemical properties. So, through this study, it can expect the yield loss by linking disease parameters, yield components and chemical properties. Principal component analysis showed that the most important components were FRS (%) and AUDPC, as they contributed 88.607% of the total cumulative variance and a prediction equation may link all the components together.

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%.

A chatbot is an application of artificial intelligence in natural language processing and speech recognition. It is a computer program that imitates humans in making conversations with other people. Chatbots that specialize in a single topic, such as agriculture, are known as domain-specific chatbots. In this paper, we present a dataset for farmer intents. Intent identification is the first step in building a chatbot. The dataset includes five intents (pest or disease identification, irrigation, fertilization, weed identification, and plantation date). The length of the dataset is 720 records. We applied a Multi-Layers Perceptron (MLP) for intent classification. We tried different numbers of neurons per hidden layer and compared between increasing the number of neurons with the fixed number of epochs. The result shows that as the number of neurons in the hidden layers increases, the introduced MLP achieves high accuracy in a small number of epochs. MLP achieves 97% accuracy on the introduced dataset when the number of neurons in each hidden layer is 256 and the number of epochs is 10.

Global climate change refers to changes in the long-term weather patterns that characterize the world's regions. The impact of climate change on agriculture is one of the major factors influencing future food security. Changing in temperature leads to outbreaks of pests and diseases thereby reducing plant production. Predicting plant pests and diseases can protect plants from loss by avoiding and controlling the predicted insects and diseases. This research introduces an Intelligent Decision Support System for insects Prediction Framework (IDSSIPF). The proposed model predicts the period in which insects can affect the plant, in addition to alarming farmers about the needed actions to mitigate climate change. IDSSIPF was experimented with to predict the affected insect period in 2019 years. The result of the experiment shows that the prediction started from a real infection period. so decision-makers can use IDSSIPF to mitigate the insects and avoid crop loss and increase productivity. Comparing the prediction results of IDSSIPF with the real periods in 2019, the accuracy of IDSSIPF is 86%.