Geosensors to Support Crop Production: Current Applications and User Requirements

Abstract

Sensor technology, which benefits from high temporal measuring resolution, real-time data transfer and high spatial resolution of sensor data that shows in-field variations, has the potential to provide added value for crop production. The present paper explores how sensors and sensor networks have been utilised in the crop production process and what their added-value and the main bottlenecks are from the perspective of users. The focus is on sensor based applications and on requirements that users pose for them. Literature and two use cases were reviewed and applications were classified according to the crop production process: sensing of growth conditions, fertilising, irrigation, plant protection, harvesting and fleet control. The potential of sensor technology was widely acknowledged along the crop production chain. Users of the sensors require easy-to-use and reliable applications that are actionable in crop production at reasonable costs. The challenges are to develop sensor technology, data interoperability and management tools as well as data and measurement services in a way that requirements can be met, and potential benefits and added value can be realized in the farms in terms of higher yields, improved quality of yields, decreased input costs and production risks, and less work time and load.

Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model

“…This study shows that integrating remote sensing data into a crop model can improve site-specific maize yield estimations as compared to the stand-alone crop modeling approach. and geographic information systems (GIS) have greatly enabled digital data-driven approaches for site-specific crop yield estimation [7][8][9][10]. Frequently used approaches include yield maps [11,12], remote sensing images [13][14][15], and process-based crop simulation models [16,17].Sensors mounted on combine harvesters calculate the mass of grain per unit of area harvested, which together with GPS receivers provide grain yield measurements at geo-referenced points to produce yield maps that are effective in visualizing spatial variability of crop yield [18].…”

Determinants of adoption of drought adaptations among vegetable growers in Florida

“…Growers can use a weather station, either on their farm or in their region, to measure precipitation, wind, temperature, and/ or atmospheric moisture levels (Thessler et al 2011). With an on-farm station, the information can be used with computer software to calculate the optimal irrigation moment.…”

Representing Situational Knowledge for Disease Outbreaks in Agriculture

“…The software system presented here evolved from an earlier prototype for the computation and visualization of disease pressure in agriculture (Thessler et al, 2011). Earlier, the computation was on weather observation data by the SoilWeather WSN (Kotamäki et al, 2009) and the result of computation was a static color-coded GIS map with coarse information for disease pressure in the region.…”