User-Technological Index of Precision Agriculture: Data Collection and Visualization

Author(s):  
Jan Masner ◽  
Jan Jarolímek ◽  
Michal Stočes ◽  
Pavel Šimek ◽  
Jiří Vaněk ◽  
...  
Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 817 ◽  
Author(s):  
Dan Popescu ◽  
Florin Stoican ◽  
Grigore Stamatescu ◽  
Loretta Ichim ◽  
Cristian Dragana

The growing need for food worldwide requires the development of a high-performance, high-productivity, and sustainable agriculture, which implies the introduction of new technologies into monitoring activities related to control and decision-making. In this regard, this paper presents a hierarchical structure based on the collaboration between unmanned aerial vehicles (UAVs) and federated wireless sensor networks (WSNs) for crop monitoring in precision agriculture. The integration of UAVs with intelligent, ground WSNs, and IoT proved to be a robust and efficient solution for data collection, control, analysis, and decisions in such specialized applications. Key advantages lay in online data collection and relaying to a central monitoring point, while effectively managing network load and latency through optimized UAV trajectories and in situ data processing. Two important aspects of the collaboration were considered: designing the UAV trajectories for efficient data collection and implementing effective data processing algorithms (consensus and symbolic aggregate approximation) at the network level for the transmission of the relevant data. The experiments were carried out at a Romanian research institute where different crops and methods are developed. The results demonstrate that the collaborative UAV–WSN–IoT approach increases the performances in both precision agriculture and ecological agriculture.


Drones ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 71 ◽  
Author(s):  
Hanno Hildmann ◽  
Ernö Kovacs ◽  
Fabrice Saffre ◽  
A. F. Isakovic

Unmanned Aerial Vehicles (UAVs) with acceptable performance are becoming commercially available at an affordable cost. Due to this, the use of drones for real-time data collection is becoming common practice by individual practitioners in the areas of e.g., precision agriculture and civil defense such as fire fighting. At the same time, as UAVs become a house-hold item, a plethora of issues—which can no longer be ignored and considered niche problems—are coming of age. These range from legal and ethical questions to technical matters such as how to implement and operate a communication infrastructure to maintain control over deployed devices. With these issues being addressed, approaches that focus on enabling collectives of devices to operate semi-autonomously are also increasing in relevance. In this article we present a nature-inspired algorithm that enables a UAV-swarm to operate as a collective which provides real-time data such as video footage. The collective is able to autonomously adapt to changing resolution requirements for specific locations within the area under surveillance. Our distributed approach significantly reduces the requirements on the communication infrastructure and mitigates the computational cost otherwise incurred. In addition, if the UAVs themselves were to be equipped with even rudimentary data-analysis capabilities, the swarm could react in real-time to the data it generates and self-regulate which locations within its operational area it focuses on. The approach was tested in a swarm of 25 UAVs; we present out preliminary performance evaluation.


2019 ◽  
Vol 8 (S2) ◽  
pp. 20-23
Author(s):  
G. Yogeswari ◽  
A. Padmapriya

Agriculture is the soul of every nation, where it considers some factors such as uneven rainfall, changing climate and weather conditions, monsoon for soil and nutrient during the crop growth. Agriculture is predominantly essential and the main source of our livelihood. Nutrient management is a major thirst area and to be the focus in the field of agriculture. Due to the paucity of nutrients in plants, the human is forced to face many challenges in day-to-day life. Restoration of nutrient is crucial, in this view there is need to espouse the precision agriculture system which alters crop related plan and policies. The main aim of this research is to collect some of the factors influencing nutrients in plant growth and analyze them. The data collection is done by both manual and precision methods. The plant chosen for the analysis is Tomato – a horticulture crop. This is an attempt towards developing an expert system based on precision data.


Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3831
Author(s):  
Padma Balaji Leelavinodhan ◽  
Massimo Vecchio ◽  
Fabio Antonelli ◽  
Andrea Maestrini ◽  
Davide Brunelli

Agriculture faces critical challenges caused by changing climatic factors and weather patterns with random distribution. This has increased the need for accurate local weather predictions and weather data collection to support precision agriculture. The demand for uninterrupted weather stations is overwhelming, and the Internet of Things (IoT) has the potential to address this demand. One major challenge of energy constraint in remotely deployed IoT devices can be resolved using weather stations that are energy neutral. This paper focuses on optimizing the energy consumption of a weather station by optimizing the data collected and sent from the sensor deployed in remote locations. An asynchronous optimization algorithm for wind data collection has been successfully developed, using the development lifecyle specifically designed for weather stations and focused on achieving energy neutrality. The developed IoT weather station was deployed in the field, and it has the potential to reduce the power consumption of the weather station by more than 60%.


Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3479 ◽  
Author(s):  
Michael Nekrasov ◽  
Ryan Allen ◽  
Irina Artamonova ◽  
Elizabeth Belding

Rural IoT sensor networks, prevalent in environmental monitoring and precision agriculture, commonly operate over some variant of the IEEE 802.15.4 standard. Data collection from these networks is often challenging, as they may be deployed in remote regions where existing backhaul infrastructure is expensive or absent. With the commercial and industrial success of Unmanned Aircraft Systems (UAS), there is understandable interest in using UASs for delay tolerant data collection from 802.15.4 IoT sensor networks. In this study, we investigate how to optimize 802.15.4 networks for aerial data collection, which, unlike other wireless standards, has not received rigorous evaluation for three-dimensional aerial communication. We analyze experimental measurements from an outdoor aerial testbed, examining how factors, such as antenna orientation, altitude, antenna placement, and obstruction, affect signal strength and packet reception rate. In our analysis, we model and predict the quality of service for aerial data collection, based on these network configuration variables, and contrast that with the Received Signal Strength Indication (RSSI)—a commonly used signal strength metric. We find that network configuration plays a significant role in network quality, which RSSI, a mediator variable, struggles to account for in the presence of high packet loss. We conclude with a discussion of strategies for optimizing sensor network configuration for aerial data collection, in light of our results.


2006 ◽  
Vol 321-323 ◽  
pp. 1213-1216
Author(s):  
Sun Ok Chung ◽  
Byong Hak Chong ◽  
Suk Won Kang ◽  
Gi Young Kim

Precision agriculture, also called as site-specific crop (or field) management, is a recent trend in crop production that uses field information collected at different within-field locations to optimize amount, timing, and location of agricultural inputs according to the site-specific requirements. Recent development of soil property sensors has facilitated sensor-based data collection for SSCM in many countries around the world. In this study, commercial soil strength, electrical conductivity, and water content and temperature sensors were applied to a Korean rice (Oriza Sativa L) field and spatial and non-spatial statistical techniques were used to assess soil conditions and the variability, and investigate optimum sampling intensity. Results of the study would be useful for establishment of data collection schemes and better application of soil property sensors to Korean paddy fields for successful precision agriculture.


Environmental and field parameters have high impact on agricultural productivity. The environmental parameters include temperature, humidity, rainfall and wind direction etc. whereas field parameters include salinity, nutrient content, oxygen levels, soil type, soil PH and soil moisture etc. Among them, Soil pH and moisture are highly correlated which can affect soil salinity, nutrient level and soil conductivity. So, these two parameters need to be measure precisely to take management decisions. Till now the process which is applied to measure soil parameters are entirely depends on laboratory testing of soil sample. This process has some overhead like availability of laboratory, manpower and cost. To overcome these challenges, we developed a virgin sensor based automated data collection technique which maintains the agricultural productivity with sustainable development. But practically sensor-based data retain some error which defers from laboratory result. In this paper, we have developed a new efficient technique to reduce the error in calculating pH value using sensors. In this research work, ten different types of soils are tested in laboratory to get the actual (pH level in soil. These values are considered as the ground truth for the experiment. The pH values of all the ten types of soil are collected from the field using wireless sensor. Our proposed mathematical model reduces the error to 0.01 between collected values and ground truth values with back propagation method based on soil moisture, environmental temperature and humidity. Here, the proposed model is empirically tested by taking some real field data values.


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