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.

<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>

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.

In this paper an image is hidden in another image using one of the hiding algorithms (Least Significant Bit) to produce the stego-cover image which used as an input with the cover to Radial basis function Network to produce the weights.Cover is delivered once to the recipient who can use it for unlimited number of messages. The weights are delivered to the recipient for each hidden message as a key. The recipient uses the cover with the weights to unhide the message. So that this method include two levels of security. The first one is hiding the message in the cover to produce stego-cover image. The second one is ciphering the embedded image using RBF Neural Network. This Network is considered as a target and the input to the Neural Network is the cover image. Then the weights, which represent the encrypted information are reconstructed. The recipient can use RBF Network to unhide the message by having the stego-cover image then the message.Matlab R2008a was used in this paper.

Image segmentation is one of the important stages in computer vision which is necessary for various applications such as robot control and identification of military targets, as will as image analysis of remote sensing applications.In this paper the segmentation is implemented using k-means algorithm and minimum distance with and without SOM. Segmentation with SOM is done via many stages. In the first stage initialization and reading of image is done as well as type identification and normalization.In the second stage the neural network SOM is implemented on the resultant image to extract its main colors. In the final stage image segmentation is done by clustering method using k-means algorithm with minimum distance. Segmentation is implemented by the following steps:-❖ Image is segmented into two parts using two clusters centers. ❖ Calculation of a suggested quality factor to test segmentation quality for that number of clusters. ❖ Increment number of a clusters by one, calculate a new quality factor and compare it with the previous segmentation quality factor. Iterate this until the quality factor degrades and consider the previous classification as the right one. ❖ When fixing right clusters centers, a new image is created by substitution of image pixel with cluster center value that is nearest to the pixel value and then displaying and saving the final image. Finally comparison is done between the four cases of results. It has been shown from result that the use of SOM with k-means & Minimum distance algorithm in feasible, since it is depends on the variation of objects components of image.