Optimized power and water allocation in smart irrigation systems

Agriculture has a significant role in countries’ economy, but irrigation process consumes both power and water resources. Since in agriculture the goal is to maximize crop’s yields with minimize costs, it is important to design a national smart irrigation system with optimal allocation of power and water resources especially in a plantation area with little rains. In this work, an optimized on-demand smart irrigation system is proposed to manage the allocation of the consumed power and water in agriculture field. The system controls irrigation process by utilizing Wireless Sensor Network (WSN) to collect real-time data from the field using sensors. Raspberry pi takes appropriate decision about irrigation process according to received data from sensor nodes, and commands are sent from it to actuator nodes. Secured Message Queuing Telemetry Transport (MQTT) protocol with Transport Layer Security (TLS) authentication protocol is used in managing the data exchange in the network over Wi-Fi technology. In addition, an optimal power and water consumptions formula is derived using Lagrange Multiplier method to allocate resources in an optimal way depending on watering demands. Both theoretical and practical results approve the efficiency of the proposed system in managing irrigation process optimally

Development of Enhanced Weed Detection System with Adaptive Thresholding, K-Means and Support Vector Machine

This paper proposes a sophisticated classification process to segment the leaves of carrots from weeds (mostly Chamomile). In the early stages, of the plants’ development, both weeds and carrot leaves are intermixed with each other and have similar color texture. This makes it difficult to identify without the help of the domain experts. Therefore, it is essential to remove the weed regions so that the carrot plants can grow without any interruptions. The process of identifying the weeds become more challenging when both plant and weed regions overlap (inter-leaves). The proposed system addresses this problem by creating a sophisticated means for weed identification. The major components of this system are composed of three processes: Image Segmentation, Feature Extraction, and Decision-Making. In the Image Segmentation process, the input images are processed into lower units where the relevant features are extracted. In the second proposed method, K-Means clustering is applied to extract the images that will be used for the identification process. The images are then normalized into a binary image using Otsu’s Thresholding. Next, in the Feature Extraction stage, relevant information of the weed and leaves are extracted from the lower unit images. Furthermore, to extract the information from the Region of Interest (ROI), Histogram of Oriented Gradient (HoG) is used to locate and label all the weed and carrot leaves regions. In the Decision-Making process, the system makes use of Support Vector Machine (SVM), which is a supervised learning algorithm, is used to analyze and segregate the weeds from the plants. Afterward, the findings are used to dictate which plants receive herbicides and which do not. The main priority for the Image Segmentation process is on overlapping images where weeds need to be isolated from plants; otherwise, in the later stages, those plants cannot be used for cultivation purposes. These methods of weed detection are effective as it automates the identification process and fewer herbicides will be used, which in turn is beneficial to the environment. The evaluation of the approach was done using an open dataset of images consisting of carrot plants. The system was able to achieve 88.99% accuracy for weed classification using this dataset. Further improvement of the proposed method successfully classifies the plant regions at a success rate of 92%. These methodologies will help reduce the use of herbicides while improving the performance and costs of Precision Agriculture

Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered