A privacy preserving association rule mining algorithm was introducedThis algorithm preserved privacy of individual values by computing scalar productMeanwhile the algorithm of computing scalar product was given and the security was analyzed

Privacy preserving association rule mining in vertically partitioned data

Wireless sensor networks (WSNs) can be used in agriculture to provide farmers with a large amount of information. Precision agriculture (PA) is a management strategy that employs information technology to improve quality and production. Utilizing wireless sensor technologies and management tools can lead to a highly effective, green agriculture. Based on PA management, the same routine to a crop regardless of site environments can be avoided. From several perspectives, field management can improve PA, including the provision of adequate nutrients for crops and the wastage of pesticides for the effective control of weeds, pests, and diseases. This review outlines the recent applications of WSNs in agriculture research as well as classifies and compares various wireless communication protocols, the taxonomy of energy-efficient and energy harvesting techniques for WSNs that can be used in agricultural monitoring systems, and comparison between early research works on agriculture-based WSNs. The challenges and limitations of WSNs in the agricultural domain are explored, and several power reduction and agricultural management techniques for long-term monitoring are highlighted. These approaches may also increase the number of opportunities for processing Internet of Things (IoT) data.

https://www.mdpi.com/1424-8220/17/8/1781/pdf

Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review.

http://www.dxsx.net/admin/editor/upload/201412/2014122915411949.pdf

Digital image forgery detection using passive techniques: A survey

Water management is paramount in countries with water scarcity. This also affects agriculture, as a large amount of water is dedicated to that use. The possible consequences of global warming lead to the consideration of creating water adaptation measures to ensure the availability of water for food production and consumption. Thus, studies aimed at saving water usage in the irrigation process have increased over the years. Typical commercial sensors for agriculture irrigation systems are very expensive, making it impossible for smaller farmers to implement this type of system. However, manufacturers are currently offering low-cost sensors that can be connected to nodes to implement affordable systems for irrigation management and agriculture monitoring. Due to the recent advances in IoT and WSN technologies that can be applied in the development of these systems, we present a survey aimed at summarizing the current state of the art regarding smart irrigation systems. We determine the parameters that are monitored in irrigation systems regarding water quantity and quality, soil characteristics and weather conditions. We provide an overview of the most utilized nodes and wireless technologies. Lastly, we will discuss the challenges and the best practices for the implementation of sensor-based irrigation systems.

https://www.mdpi.com/1424-8220/20/4/1042/pdf

IoT-Based Smart Irrigation Systems: An Overview on the Recent Trends on Sensors and IoT Systems for Irrigation in Precision Agriculture

Internet of Things (IoT) plays a crucial role in smart agriculture. Smart farming is an emerging concept, because IoT sensors capable of providing information about their agriculture fields. The paper aims making use of evolving technology i.e. IoT and smart agriculture using automation. Monitoring environmental factors is the major factor to improve the yield of the efficient crops. The feature of this paper includes monitoring temperature and humidity in agricultural field through sensors using CC3200 single chip. Camera is interfaced with CC3200 to capture images and send that pictures through MMS to farmers mobile using Wi-Fi.

IOT Based Monitoring System in Smart Agriculture

Internet Of Things (IoT)is a shared network of objects or things which can interact with each other provided the internet connection. IoT plays an important role in agriculture industry which can feed 9.6 billion people on the Earth by 2050. Smart Agriculture helps to reduce wastage, effective usage of fertilizer and thereby increase the crop yield. In this work, a system is developed to monitor crop-field using sensors (soil moisture, temperature, humidity, Light) and automate the irrigation system. The data from sensors are sent to web server database using wireless transmission. In server database the data are encoded in JSON format. The irrigation is automated if the moisture and temperature of the field falls below the brink. In greenhouses light intensity control can also be automated in addition to irrigation. The notifications are sent to farmers' mobile periodically. The farmers' can able to monitor the field conditions from anywhere. This system will be more useful in areas where water is in scarce. This system is 92% more efficient than the conventional approach.

IOT based crop-field monitoring and irrigation automation

Digital image forgery detection and estimation by exploring basic image manipulations

In today’s world, due to irregular climatic patterns and other environmental issues various pests will affect the crops. These issues may affect the soil nutrition too. Due to this deficiency in nutrition, several diseases may affect the crops. In large agricultural field farmers feel difficult to monitor the pest in every nook and corner. Before identifying the pests, it may spread over vast area and cause severe damage to crops. Also they may not aware of bacterial disease that may affect the crops and their symptoms. Besides farmers may not know which type of pesticide and chemicals they need to use to prevent the further damage of crops. In some cases excess usage of those pesticides and other chemicals may affect the corn fields. Improper usage of those chemicals may affect the yield. To monitor the field periodically we need more human power. These problems are overcome by our proposed system. In our system diseases/pests are identified at early stages by capturing the images periodically in the agricultural field. Using image processing techniques the captured image will be segmented. For this purpose Texture based Segmentation and Simple Linear Iterative Clustering (SLIC) are used. From the segmented images the features for identifying pests and diseases will be extracted. The extracted features are used for classification. The presence of pest/disease will be identified at the first stage. In second stage the type of pest/disease will be detected. For classification both Binary Support Vector Machine (BSVM) and Multi class Support Vector Machine (MSVM) are used. And also, the pesticides and other chemicals which are needed to protect the field from further damage will be recommended along with the amount of chemicals needed and the method of usage of those chemicals.

Agro Suraksha: pest and disease detection for corn field using image analysis

An Extensive growth in software technologies available on Internet results in tampering of images. The main problem in the real world is to determine whether an image is real or forged. In this paper we propose a new method that makes use of the codebook which is generated from the set of image features to determine the geometric manipulations that are occurred in the received image. In the proposed method, an image hash based on bag of visual words attached as signature to the image before transmission. At the destination the forensic hash is compared to detect the geometric manipulations that are applied to the received image. The spatial distribution of image features is encoded to deal with highly textured and contrasted tampering patterns. The proposed method detects all types of tampering.

https://ieeexplore.ieee.org/iel7/6522357/6528464/06528568.pdf

A forensic method for detecting image forgery

In face recognition system age variation causes the serious problem. In this work discriminative model of face recognition is used to deal with age invariant problem. Feature extraction is done by densely sampled local image descriptors such as Scale Invariant Feature Transform (SIFT) and Multi scale Local Binary Pattern (MLBP) which gives the discriminatory information useful to detect the edge direction of the image which is robust to illumination, pose, expression and occlusion. SIFT and MLBP features used to recognize the occluded face images also. Since the extracted features are with high dimensionality new technique called Multi Feature Discriminant Analysis (MFDA) is used to reduce the feature space. MFDA is similar to LDA, where multiple features are combined with two different random sampling methods, multiple LDA based classifiers are constructed and then result from each classifier are combined by fusion rule for final decision. Another method to reduce the dimensionality of extracted features is PCA and classification is done by nearest neighbor method. Finally the results of two methods are compared. Images are taken from FG-NET database.

S. Devi Mahalakshmi

Papers

IOT based crop-field monitoring and irrigation automation

Digital image forgery detection and estimation by exploring basic image manipulations

Agro Suraksha: pest and disease detection for corn field using image analysis

A forensic method for detecting image forgery

Age invariant face recognition with occlusion