Guilherme Martineli Sanches

A computational environment to support research in sugarcane agriculture

Abstract Sugarcane is an important crop for tropical and sub-tropical countries. Like other crops, sugarcane agricultural research and practice is becoming increasingly data intensive, with several modeling frameworks developed to simulate biophysical processes in farming systems, all dependent on databases for accurate predictions of crop production. We developed a computational environment to support experiments in sugarcane agriculture and this article describes data acquisition, formatting, storage, and analysis. The potential to support creation of new agricultural knowledge is demonstrated through joint analysis of three experiments in sugarcane precision agriculture. Analysis of these case studies emphasizes spatial and temporal variations in soil attributes, sugarcane quality, and sugarcane yield. The developed computational framework will aid data-driven advances in sugarcane agricultural research.

The potential for RGB images obtained using unmanned aerial vehicle to assess and predict yield in sugarcane fields

ABSTRACT Estimating yield is a major challenge for the majority of agricultural crops. With the advancement of field technologies however, especially those related to the use of Unmanned Aerial Vehicles (UAV) or Drones, the quality of available information has increased, making it possible to overcome technological bottlenecks. However, drone technologies have advanced much faster than studies dealing with the treatment and analysis of information, which can represent an obstacle to the complete adoption of such technologies in sugarcane fields. The objective of the present study was to evaluate the potential for UAV images to assess the degree of canopy closure from different planting approaches and row-spacing treatments applied to sugarcane crop, in order to assess the potential of these tools to predict crop yield. The vegetative growth of the crop was evaluated and the images were obtained at the point of maximum tillering and the inflection point of the biomass accumulation curve. The evaluations included the index; LAI (Leaf Area Index) and GRVI (Green-Red Vegetation Index) obtained by field sensor and UAV, respectively. Because the images from UAV cover the total area, the results revealed that GRVI appears to be much better able to reflect the whole condition of the crop yield (R2 = 0.69) in the field when compared to LAI (R2 = 0.34); demonstrated convincingly by the high spatial resolution capacity of the technology. When integrated, these two indices were able to improve yield estimates by 10% (R2 = 0.79). Images obtained using UAV can represent a low-cost tool for obtaining high-precision remote data that can be used to estimate the agricultural yield of sugarcane fields; and in this way are an effective tool to aid decision making by growers.

Improving the Kriging of Soil Attributes Using Soil Electrical Conductivity as External Drift

Summary Soil Electrical conductivity (ECa) are not directly related to the soil attributes used to make management decisions. Nevertheless, this information provide an opportunity to oriented soil sampling based on its pattern, rapidly and at a relatively low cost. This paper discuss how Kriging with external drift (KED), using low-density orientated soil sampling, based in ECa, can be used to provide reasonable soil attribute maps. The results are compared with other 4 different strategies. An example of lime recommendation at variable rate is presented. The results show that KED had clear advantages over the other methods when using fewer sample points.