Development of a Wireless Sensor Network for Monitoring Environmental Condition on a Farmland

In recent time, the wireless sensor network technology has found its implementation in precision agriculture as a result of the need for high productivity. This paper focuses on the development of a wireless sensor network on agricultural environment to monitor environmental conditions and deduce the appropriate environmental parameters required for the high yield of crop production on a given farmland. The developed wireless sensor network is built around sensor nodes and a master microcontroller (PI16F648A) that takes in the data sent from the nodes for upload into a personal computer (PC). Each node has sensors to monitor environmental conditions such as temperature, relative humidity and light intensity which are important environmental factors in an agricultural set-up. The DHT11 sensor is used to sense and provide calibrated digital outputs for the measured temperature and relative humidity while a calibrated light dependent resistor (LDR) is configured to the light intensity sensor unit. The outputs from these sensors are processed by the microcontroller and sent wirelessly, using low-power radio frequency transceivers, to a remote master controller for storage. The deployment of the developed wireless sensor network on a named farmland shows that the aforementioned could be efficiently utilized to provide an up-to-date and accurate measurement of agriculture data, which include light-intensity, relative-humidity and temperature. Thus, the developed framework replaces the traditional method of predicting environmental parameters required on a given farmland

Computational Contributions to the Automation of Agriculture

The purpose of this paper is to explore ways that computational advancements have enabled the complete automation of agriculture from start to finish. With a major need for agricultural advancements because of food and water shortages, some farmers have begun creating their own solutions to these problems. Primarily explored in this paper, however, are current research topics in the automation of agriculture. Digital agriculture is surveyed, focusing on ways that data collection can be beneficial. Additionally, self-driving technology is explored with emphasis on farming applications. Machine vision technology is also detailed, with specific application to weed management and harvesting of crops. Finally, the effects of automating agriculture are briefly considered, including labor, the environment, and direct effects on farmers

Survey of Impact of Technology on Effective Implementation of Precision Farming in India

The advancements in technology have made its impact on almost every field. India being an agricultural country, proper use of technology can greatly help in improving the standard of living of the farmers. With varying weather conditions, illiteracy of farmers and non-availability of timely assistance, the farmers of this country could not get the best out of their efforts. Precision farming focuses mainly on the aspects that can improve the efficiency based on the data collected from various sources viz. meteorology, sensors, GIS, GPS, etc. The information pertaining to farmland (e.g., soil moisture, soil pH, soil nitrogen) and agro-meteorology (e.g., temperature & humidity, solar radiation, wind speed, atmospheric CO2 concentration, rainfall, climate change and global warming) are used as input parameters to decide the varying requirements of the crop cultivation. Historical farm land data are used as a means to decide on the kind of actions to be taken under a specific scenario. This paper surveys the existing methods of precision farming and highlights the impact of technology in farming. An overview of different technologies used in precision farming around the world and their implications on the yield are discussed. The methods adopted towards managing different types of crops, the varying environmental conditions and the use of realtime data being collected through sensors are also analyzed. Also, the need for dynamic approaches to assist the farmers in taking context specific decisions has been highlighted

Solar Powered WSN for monitoring environment and soil parameters by specific app for mobile devices usable for early flood prediction or water savings

This paper describes the design and realization of a smart electronic system, based on a Wireless Sensor Network, for wide-area monitoring of availability level and rapid changes of the water presence in the monitored soil in order to guarantee flood prediction, water savings in the optimized farmland irrigation, waste reduction and optimal use of water resources where its availability is low. The designed sensor node, by means of the different embedded sensors, is capable of detecting environmental parameters, the solar radiation level and soil temperature and moisture (i.e. water volume content) values. The sensors communicate with a central processing unit located on board, used both as data processing unit and as Wi-Fi transceiver to receive/transmit the sensors data. The user near a sensor node, by a tablet or smartphone with an appropriate app, can collect information provided from sensors and share them with all users who use the same app on the Cloud, through peer to peer Wi-Fi or other internet connection

A Data Collecting Strategy for Farmland WSNs using a Mobile Sink

To the characteristics of large number of sensor nodes, wide area and unbalanced energy consumption in farmland Wireless Sensor Networks, an efficient data collection strategy (GCMS) based on grid clustering and a mobile sink is proposed. Firstly, cluster is divided based on virtual grid, and the cluster head is selected by considering node position and residual energy. Then, an optimal mobile path and residence time allocation mechanism for mobile sink are proposed. Finally, GCMS is simulated and compared with LEACH and GRDG. Simulation results show that GCMS can significantly prolong the network lifetime and increase the amount of data collection, especially suitable for large-scale farmland Wireless Sensor Networks

Root Zone Sensors for Irrigation Management in Intensive Agriculture

Crop irrigation uses more than 70% of the world’s water, and thus, improving irrigation efficiency is decisive to sustain the food demand from a fast-growing world population. This objective may be accomplished by cultivating more water-efficient crop species and/or through the application of efficient irrigation systems, which includes the implementation of a suitable method for precise scheduling. At the farm level, irrigation is generally scheduled based on the grower’s experience or on the determination of soil water balance (weather-based method). An alternative approach entails the measurement of soil water status. Expensive and sophisticated root zone sensors (RZS), such as neutron probes, are available for the use of soil and plant scientists, while cheap and practical devices are needed for irrigation management in commercial crops. The paper illustrates the main features of RZS’ (for both soil moisture and salinity) marketed for the irrigation industry and discusses how such sensors may be integrated in a wireless network for computer-controlled irrigation and used for innovative irrigation strategies, such as deficit or dual-water irrigation. The paper also consider the main results of recent or current research works conducted by the authors in Tuscany (Italy) on the irrigation management of container-grown ornamental plants, which is an important agricultural sector in Italy

Benefits of pollution monitoring technology for greenhouse gas offset markets

Environmental economists have shown that tradable emission permit markets can reduce the costs to society of pollution reduction. However, when emissions are difficult to monitor and verify, offset credits from pollution reductions may be subject to price discounts that reduce social welfare. In this paper, we estimate the extent to which social welfare could be improved by using new technology to increase the accuracy with which pollution flows from agricultural fields can be monitored. We use a hypothetical case study of a situation in which farmers can reduce nitrous oxide (N2O) emissions from Midwest agricultural land parcels and sell the resulting offset permits in a greenhouse gas tradable permit market. We simulate market outcomes with and without an inexpensive technology that increases the accuracy of emission estimates, reduces the discount to which agricultural offset permits are subject, and improves the performance of tradable permit system. We find that the benefits from such technology range as high as $138 for a 100 acre field if N2O emissions are an exponential function of nitrogen application rates. However, variation in the benefits to farmers of eliminating price discounts may mean efficient technology adoption is not uniform across space.tradable permit, greenhouse gases, uncertainty, technology

Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture

The use of sensors and the Internet of Things (IoT) is key to moving the world\u27s agriculture to a more productive and sustainable path. Recent advancements in IoT, Wireless Sensor Networks (WSN), and Information and Communication Technology (ICT) have the potential to address some of the environmental, economic, and technical challenges as well as opportunities in this sector. As the number of interconnected devices continues to grow, this generates more big data with multiple modalities and spatial and temporal variations. Intelligent processing and analysis of this big data are necessary to developing a higher level of knowledge base and insights that results in better decision making, forecasting, and reliable management of sensors. This paper is a comprehensive review of the application of different machine learning algorithms in sensor data analytics within the agricultural ecosystem. It further discusses a case study on an IoT based data-driven smart farm prototype as an integrated food, energy, and water (FEW) system