Egypt is suffering from water scarcity and tried to pursue a package of policies to decrease the water deficit, by using un¬conventional resources like treated wastewater. Wastewater is 6.9 billion m3/year, the total of treated sewage is 5.1 billion m3 in 2019/20. The wastewater reuse potential in agriculture is 1 billion m3/year which leads us to question: What is the economic return on the use of treated sewage to reduce the water deficit and achieve targeted development? This paper aims to identify the value of investment projects in the sewage treatment sector and determine the cost and returns of treated wastewater in Agriculture by using (CBA) approach and indicators of financial analysis. The results prove that the binary stage is a commit¬ment to the state and its responsibility to preserve the environment, the ratio of reuse of sewage to the total cost of wastewater treatment is 12%, while the ratio of the cost of compulsory treatment to the state to the total cost of wastewater treatment is 88%. It is expected that value add will increase and the benefits will be 22.5 billion L.E. The paper recommends encouraging investors to the wastewater treatment sector by setting up treatment units on their farms. NPV for the project is 388.71 Million L.E which means the project is greater than its cost, profitable, and, if the firm accepts the project, then the value of the firm would increase. PI for the project equals 1.2 Million L.E which means the investment in this project would be profitable and should be implemented and IRR is 16.4%, It is greater than the rate of cost of capital 14%, then investment in the concerned project would be profitable

Cultivation and production of sesame within the framework of contract farming - Technical details of cultivation

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%.

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.

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.

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In light of the foregoing results, the following can be recommended:
1. Re-focusing on the cultivation of high-quality varieties according to the irrigation water available for the rice crop using technological methods and thus increasing the production of rice along with horizontal expansion in accordance with the government's plan for the cultivation of one and a half million acres.
2. Activating the role of agricultural extension and cooperatives in guiding farmers to the correct use of water and rationalizing its use, especially in the cultivation of the rice crop.
3. Work to improve the productivity of the wheat crop in the Kafr El-Sheikh governorate by following the different technological methods and choosing the appropriate varieties and the appropriate method of cultivation and harvesting of the wheat crop, which results in an increase in production in the governorate in particular and in Egypt in general.
4. The necessity of reviewing the agricultural policies related to cotton and rice crops, especially the legislation that allocates the areas and varieties of cotton and the way it is traded, and not to leave the cotton seed trade to the private sector.
5. The necessity of working on the price stability of the cotton crop, thus stabilizing the areas and then stabilizing production and exports.
6. Establishing a fixed marketing policy for the cotton crop through the contractual policy and announcing the guarantee prices to farmers well before planting dates so that the farmer can respond to the policy of the state represented in the Ministry of Agriculture and Land Reclamation.
7. Using agricultural research institutes for study crops to devise varieties that are resistant to climatic changes that cause a decrease in production in general in Egypt, especially in Kafr El-Sheikh governorate, and also try to devise varieties of rice with less water consumption.
8. Providing production requirements in agricultural associations in order to reduce their prices in the markets, which causes an increase in production costs for the crops under study for the farmers, and then the farmers tend to plant other crops.
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Master Thesis: Department of Agricultural Economics, Faculty of Agriculture, Mansoura University, 2022.
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