An intelligent irrigation system based on internet of things (IoT) to minimize water loss

This paper proposes a comparison of three machine learning algorithms for a better intelligent irrigation system based on internet of things (IoT) for differents products. This work's major contribution is to specify the most accurate algorithm among the three machine learning algorithms (k-nearest neighbors (KNN), support vector machine (SVM), artificial neural network (ANN)). This is achieved by collecting irrigation data of a specific products and split it into training data and test data then compare the accuracy of the three algorithms. To evaluate the performance of our algorithm we built a system of IoT devices. The temperature and humidity sensors are installed in the field interact with the Arduino microcontroller. The Arduino is connected to Raspberry Pi3, which holds the machine learning algorithm. It turned out to be ANN algorithm is the most accurate for such system of irrigation. The ANN algorithm is the best choice for an intelligent system to minimize water loss for some products.

A smart Agriculture Irrigation System using sensor array based IOT

The role of Agriculture is important to build a nation, since more than 58% of the population in our country is dependent on agriculture that means half of the population is investing in agriculture. However, many farmers are unfamiliar with intelligent irrigation systems designed to improve the water used for their crops. The proposed system is to precisely monitor the distribution of the water to crops. This IOT based system has a distributed wireless network of soil moisture sensors to monitor soil moisture. Other sensors such as temperature, humidity, rain, IR, LDR, foot. The gateway device also processes the detector’s information and transmits the data to the farmer. An algorithm was developed using threshold values for soil moisture and nutrients, and these values were programmed into a node com-based gateway to control water for irrigation. Complete sensor data is sent to the free cloud using NODEMCU and displayed on websites and apps. This proposed work presents extensive research on irrigation systems in smart agriculture.

A review on a machine learning approach of an intelligent irrigation monitoring system with edge computing and the internet of things

Water consumption during irrigation has been a much-researched area in agricultural activities, and due to the frugal nature of different practiced irrigation systems, quite a sufficient amount of water is wasted. As a result, intelligent systems have been designed to integrate water-saving techniques and climatic data collection to improve irrigation. An innovative decision-making system was developed that used Ontology to make 50% of the decision while sensor values make the remaining 50%. Collectively, the system bases its decision on a KNN machine learning algorithm for irrigation scheduling. It also uses two different database servers, an edge and an IoT server, along with a GSM module to reduce the burden of the data transmission while also reducing the latency rate. With this method, the sensors could trace and analyze the data within the network using the edge server before transferring it to the IoT server for future watering requirements. The water-saving technique ensured that the crops obtained the required amount of water to ensure crop growth and prevent the soil from reaching its wilting point. Furthermore, the reduced irrigation water also limits the potential runoff events. The results were displayed using an android application.

Will the Adoption of Early Fertigation Techniques Hinder Famers’ Technology Renewal? Evidence from Fresh Growers in Shaanxi, China

Fertigation technology is key to solve water pollution and inefficient fertilizer use. However, some early techniques cannot adapt to the current situation of labor shortages and large-scale planting. Therefore, it is necessary to consider farmers’ willingness to adopt more adaptive techniques. Specifically, we focus on whether early technology adoption will hinder technology renewal and whether the factors affecting the adoption of early and latest techniques are consistent. Through theoretical analysis and a survey, we find that farmers’ endowments such as income and labor force only affect the adoption intentions to the high-cost technique (Intelligent Irrigation Control System), but not early techniques (Venturi injector and Differential pressure tank), while farmers’ information processing ability and information acquisition channels affect both. Finally, the results of Propensity Score Matching show that early technology adoption will not become an obstacle to technology renewal.

An IoT Fast and Low Cost Based Smart Irrigation Intelligent System Using a Fuzzy Energy-Aware Routing Approach

Agriculture plays a major role in the world economy and most people depend on it for their livelihood. This makes water an important resource that must be conserved using the latest available technologies. Today, the Internet of Things (IoT) has extended its capabilities to smart farming. In this paper, an automated and low-cost system for intelligent irrigation based on a Fuzzy-based energy-aware routing approach is presented. In addition, a neural network is trained to determine the best irrigation program, based on information received from sensors (such as temperature, soil moisture, etc.). The user in the system can monitor the data collection process with mobile phones, mobile computers, etc. and manage the irrigation of agricultural products. The proposed system proves its suitability with intelligence and low cost and portability, for greenhouses, farms, etc. The simulation results show that the proposed method offers better results compared to the LEACH protocol as well as the WSN-IoT algorithm in various criteria such as network lifetime and power consumption.