Sirpa Thessler

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.

Sensor networks are increasingly being implemented for environmental monitoring and agriculture to provide spatially accurate and continuous environmental information and (near) real-time applications. These networks provide a large amount of data which poses challenges for ensuring data quality and extracting relevant information. In the present paper we describe a river basin scale wireless sensor network for agriculture and water monitoring. The network, called SoilWeather, is unique and the first of this type in Finland. The performance of the network is assessed from the user and maintainer perspectives, concentrating on data quality, network maintenance and applications. The results showed that the SoilWeather network has been functioning in a relatively reliable way, but also that the maintenance and data quality assurance by automatic algorithms and calibration samples requires a lot of effort, especially in continuous water monitoring over large areas. We see great benefits on sensor networks enabling continuous, real-time monitoring, while data quality control and maintenance efforts highlight the need for tight collaboration between sensor and sensor network owners to decrease costs and increase the quality of the sensor data in large scale applications.

Aim We present a new method to economically map gradual changes in plant species composition in lowland rain forests using field data and satellite images. Such a method will be a useful tool in planning the sustainable use and conservation of Amazonian rain forests.Location The study covered an area ofc.700 km²of primary rain forest in Amazonian Ecuador.Methods We field inventoried the species composition of pteridophytes and Melastomataceae in 340 inventory plots (5 m × 50 m), described the prevailing topography and analysed soil cation concentration and texture. We used non‐metric multidimensional scaling (NMDS) to summarize the floristic variation among the inventory plots in three ordination dimensions. The scores of the three ordination axes were predicted to non‐visited places using a Landsat TM (thematic mapper) satellite image and theknearest neighbours (knn) estimation method. To avoid extrapolation, we excluded from the analysis those pixel windows whose spectral values were not represented in the areas covered by field sampling. The accuracy of the predictions was evaluated by cross‐validation and by comparing the predictions based on spectrally nearest neighbours to the predictions based on random neighbours.Results The floristic gradients presented by NMDS ordination were interpretable in terms of topography, drainage and soil cation content. Thirteen percent of the cloud‐free pixels were excluded from theknn analysis to avoid extrapolation. The estimates of the floristic ordination scores based on spectrally nearest neighbours were always more accurate than estimates based on random neighbours.Main conclusions The presented method needs a relatively small input of work and resources, is mechanistic and produces maps that give relevant information on floristic variation over forest areas that are traditionally considered essentially homogeneous. Therefore, the method appears to have a great potential for use in mapping large areas of Amazonian rain forests.