No abstract

Efficient and cost effective ways of irrigation have emerged as the need of the hour due to limited sweet water resources, especially the countries that are seriously hit by a lack of sweet water reservoirs. The majority of the water is wasted due to inefficient ways of watering plants. In this paper, we propose an intelligent approach for efficient plant irrigation that has a database of daily water needs of a type of plant and decides the amount of water for a plant type on the basis of the current moisture in soil, humidity, and time of the day. This approach not only saves sweet water by efficient utilization, but also supports smart consumption of energy. Our approach employs IoT and a set of sensors to efficiently record plant data and their watering needs and the approach is implemented with a mobile phone application interface that is used to continuously monitor and control the efficient watering system. The results of this study are easy to reproduce as the sensors used are cheap and easy to access. The study discusses in this paper is experimented on small area (such as tunnel farm) but the results of the experiments show that the used approach can be generalized and can be used for large size fields for efficient irrigation. The results of the experiments also outperform the manual approach and the similar approaches for sensor based irrigation systems.

Smart parsimonious and economical ways of irrigation have build up to fulfill the sweet water requirements for the habitants of this world. In other words, water consumption should be frugal enough to save restricted sweet water resources. The major portion of water was wasted due to incompetent ways of irrigation. We utilized a smart approach professionally capable of using ontology to make 50% of the decision, and the other 50% of the decision relies on the sensor data values. The decision from the ontology and the sensor values collectively become the source of the final decision which is the result of a machine learning algorithm (KNN). Moreover, an edge server is introduced between the main IoT server and the GSM module. This method will not only avoid the overburden of the IoT server for data processing but also reduce the latency rate. This approach connects Internet of Things with a network of sensors to resourcefully trace all the data, analyze the data at the edge server, transfer only some particular data to the main IoT server to predict the watering requirements for a field of crops, and display the result by using an android application edge.

Product fake reviews are increasing as the trend is changing toward online sales and purchases. Fake review detection is critical and challenging for both researchers and online retailers. As new techniques are introduced to catch the fake reviewer, so are their intruding approaches. In this paper, different features are amalgamated along with sentiment score to design a model that checks the model performance under different classifiers. For this purpose, six supervised learning algorithms are utilized to build the fake review detection models, utilizing LIWC, unigrams, and sentiment score features. Results show that the amalgamation of selected features is a better approach to fake review detection, achieving an accuracy score of 88.76%, which is promising compared to similar other work.