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Internet of Things (IoT) is a new fast communication technology designed to continuously make communication among different types of devices. The researchers have exerted huge efforts to employ IoT to facilitate daily life. These include IoT big issues like securing information exchange, smart agriculture systems, and general-purpose internet access. This paper shows research efforts to enable IoT-based smart agriculture. It starts with the underlying architecture, then discusses some of the recent IoT trials. It classifies the literature by deriving a taxonomy based on technologies, communication cofactors, network types, local area wireless criteria, targets, and characteristics. Moreover, it highlights the unprecedented chances brought about by IoT-based smart architectures and their impact on human life. So IoT will solve these problems by connecting soil moisture sensors, giving us a quick response to avoid crop losses through careful monitoring and remote control of the fields. Moreover, IoT facilitates and solves most agriculture-related problems, through the use of modern technologies in monitoring plant biomarkers and controlling watering processes to increase production and efficiency. This gives us a visualization of the state of the field and enables us to take the necessary measures before the problem occurs as a way of problem anticipation.

Due to the increase of development in modern technology which entered in most fields of life including sustainable agriculture; most studies revealed that most lesions result from over irrigation which causes fungi in plant and soil salinity. Recently; some very important terms emerged and changed most agricultural concepts such as the sustainable agriculture, green cities and smart irrigation systems. Most of these systems improved the quality of production and reduced lesions. In this paper a smart irrigation system was designed depending on Field Capacity F.C value, Wilting Point W.P value. In addition to the ranges of moisture that are measured in the field which are important in decision making of irrigation and selecting the best values to rely on such as threshold value in designing for the sake of maintaining moisture in the soil permanently. The best field moisture value was recorded when designing was %24 at threshold value in a clay soil field. Finally; the best types of microcontrollers ESP8266 & ESP-32S and moisture sensors, which are used to upload the data to Adafruit server. Also, the fast and light Message Queuing Telemetry Transport (MQTT) protocol, was used to transfer the ranges of moisture through the system and cloud computing.

<span>Most </span><span>of the research </span><span>showed that the reason behind the agricultural lesions is the over usage of water in irrigation the matter which cause the appearance of fungicide on plants and salinity of the soil. From this point emerged the need for adapt some systems to work in farms in order to reduces the expenses of the product, improve its quality and lessen the consumption of water. Internet webs have been a preceding means in such a scope; and they also showed flexibility in designing such systems. In this paper; a smart irrigation system that depend on the values of moisture content and the agricultural constants (Feld Capacity, Wilt Point of the plant, Bulk Density</span><span>, Depth of the root of the plant, the consumption of each water dripper and the passing area) in making the decision of irrigation and running the water pump, depending on the quantity of water to be added and the duration of irrigation time,</span><span> and it is better. Field humidity levels at 0.32</span><span>. This system was built by using the microcontroller ESP-32S&amp;ESP8266 and moister sensor. The data was uploaded to Adafruit server for the sake of remote monitoring by MQTT protocol.</span>

Deep learning approaches have shown to be useful in assisting physicians in making decisions about cancer, heart disease, degenerative brain disorders, and eye disease. In this work, a deep learning model was proposed for the diagnosis of retinal diseases utilizing optical coherence tomography X-ray pictures (OCT) to identify four states of retina disease. The proposed model consists of three different convolutional neural network (CNN) models to be used in this approach and compare the results of each one with others. The models were named respectively as 1FE1C, 2FE2C, and 3FE3C according to the design complexity. The concept uses deep CNN to learn a feature hierarchy from pixels to layers of classification retinal diseases. On the test set, the classifier accuracy is 65.60 % for a (1FE1C) Model, 86.81% for (2FE2C) Model, 96.00% for (3FE3C) Model, and 88.62% for (VGG16) Pre-Train Model. The third model (3FE3C) achieves the best accuracy, although the VGG16 model comes close. Also, this model improves the results of previous works and paves the way for the use of state-of-the-art technology of neural network in retinal disease diagnoses. The suggested strategy may have a bearing on the development of a tool for automatically identifying retinal disease.

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Retinal image analysis is crucial for The classification of retinal diseases such as "Age Related Macular Degeneration (AMD)", "Diabetic Retinopathy (DR)", "Retinoblastoma", "Macular Bunker", "Retinitis Pigmentosa", and "Retinal Detachment". The early detection of such diseases is important insofar as it contributes in mitigating future implications. Many approaches have been developed in the literature for auto-detecting of retinal landmarks and pathologies. The current revolution in deep learning techniques has opened the horizon for researchers in the field of ophthalmology. This paper is a comprehensive review of the deep learning techniques applied for the classification of retinal images, pathology, retinal landmarks, and disease classification. This review is based on indicators such as sensitivity, Area under ROC curve, specificity, F score, and accuracy.

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