Geosensors to Support Crop Production: Current Applications and User Requirements

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

Dependence of Conditioner Power Input on Mowing Machine Material Feed Rate

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a Technical Paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 5 (2003): F. Kumhala, M. Kroulik, and V. Prosek. Dependence of Conditioner Power Input on Mowing Machine Material Feed Rate. Vol. V. July 2003

Laboratorni mereni pruchodnosti materialu rotacnim zacim strojem.

An overview of different methods developed and tested for material feed rate measurement during the work of harvesting machines is done. Many systems are known and just commercially used for grain flow measurement during the grain harvest by combine harvesters. The errors and their sources during the work of these systems described by many authors are discussed. The yield measurement methods are under developing for other harvesting machines, e.g. forage harvesters, mowing machines, potato, sugar beet, cotton and tomato harvesters etc. The function principles of systems based on weighing trailers or round balers are described as well. Two methods of the mowing machine material feed rate measurement were developed and tested. The measurements carried out proved that a very good linear relationship existed between the conditioner's power input, output frequency of the apparatus measuring impact force by means of the impact plate, and material feed rate through the mowing machine. The calculated R-Squared values were about 0,95. The impact of materials and conditions changes on mowing machine material feed rate measurement accuracy was measured under laboratory conditions as well. It was evident from statistical evaluation that changing crop variety, crop maturity and intensity of conditioning can have statistically significant influence on the measurement based on torque-meter. For impact plate measurement it was find out that it is not possible to determine statistically the influence of testing factors from our measurements.Available from STL Prague, CZ / NTK - National Technical LibrarySIGLECZCzech Republi

Landsat and Sentinel-2 images as a tool for the effective estimation of winter and spring cultivar growth and yield prediction in the Czech Republic

The influence of climate and topography on crop condition and yield estimates is most effectively monitored by non-invasive satellite imagery. This paper evaluates the efficiency of free-access Sentinel 2 and Landsat 5, 7 and 8 satellite images scanned by different sensors on wheat growth and yield prediction. Five winter and spring wheat cultivars were grown between 2005 and 2017 in a relatively small 11.5 ha field with a 6% slope. The normalized difference vegetation index was derived from the satellite images acquired for later growth phases of the wheat crops (Biologische Bundesanstalt, Bundessorenamt and Chemical industry 55 – 70) and then compared with the topography wetness index, crop yields and yield frequency maps. The results showed a better correlation of data obtained over one day (R2 = 0.876) than data with a one-day delay (R2 = 0.689) using the Sentinel 2 B8 band instead of the B8A band for the near-infrared part of electromagnetic spectrum in the normalized difference vegetation index calculation