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%.

Nanotechnology is a rapidly spreading field to be applied in agriculture. From all kinds of nutrients, zinc nanoparticles
had a great interest in investigations due to its importance for plant as it involved in many growth processes. Presently, rare
literature is available about nanoparticles effects on date palm in vitro propagation, especially zinc oxide nanoparticles (ZnONPs).
Therefore, ZnO-NPs were used in this investigation to study their effect on different stages of date palm (cv. Khalas)
somatic embryo protocol. Instead of zinc sulfate (
ZnSo4) in Murashige and Skooge medium (8.9 mg/l), lower concentrations
of ZnO-NPs (0.2, 0.1, 0.05 and 0.025 mg/l) were added. Generally, results showed that ZnO-NPs accelerated superior values
compared with ZnSo4,
as a control treatment, at all stages of date palm tissue culture protocol. Lower concentrations of
ZnO-NPs (0.025 and 0.05 mg/l) gave better values compared with higher concentrations (0.1 and 0.2 mg/l) in callus growth,
globularization, embryo formation number, secondary embryo number and shoot length. Whereas, the highest concentration
showed higher shoot number and rooted plantlet length. Antioxidants enzymes and ROS and their relations were identified
in date palm leaflets. As we know it is the first investigation of ZnO-NPs and their morphological and biochemical effects
on date palm in vitro culture.

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.