Automatic detection and evaluation of sugarcane planting rows in aerial images

Sugarcane planting is an important and growing activity in Brazil.Thereupon,several techniques have been developed over the years to maximize crop productivity and profit,amongst them,processing of sugarcane field images.In this sense,this research aims to identify and analyze crop rows and measure their gaps from aerial images of sugarcane fields.For this,a small Remotely Piloted Aircraft captured the images,generating orthomosaics of the areas for analysis.Then,each orthomosaic is classified with the K-Nearest Neighbor algorithm to segment regions of interest.Planting row orientation is estimated using the RGB gradient filter.Morphological operations and computational geometry models are then used to detect and map rows and gaps along the planting row segment.To evaluate the results,crop rows are mapped and compared to manually taken measurements.Our technique obtained an error smaller than 2%when compared to gap length in crop rows from an orthomosaic with the area of 8.05 ha(ha).The proposed approach can map the positioning of the automatically generated row segments appropriately onto manually created segments.Moreover,our method also achieved similar results when confronted with a manual technique for differing growth stages(40 and 80 days after harvest)of the sugarcane crop.The proposed method presents a great potential to be adopted in sugarcane planting monitoring。

Using Support Vector Machines and neural networks to classify Merlot wines from South America

Wines with a clear geographical origin are an issue of interest for consumers and food industries.This paper presents a data mining study of Merlot wines from South America to identify the fingerprint of their geographical origin.A group of samples from Argentina(n=17),Brazil(n=12),Chile(n=48),and Uruguay(n=6)was analyzed.Twenty chemical compounds were determined by high-performance liquid chromatography(HPLC).These compounds include antioxidant activity,total polyphenols,total anthocyanins,individual anthocyanins and color.Four binary classification problems were performed(Brazil versus non-Brazil,Argentina versus non-Argentina,Chile versus non-Chile,and Uruguay versus non-Uruguay)to investigate the geographic characteristics of each country.Through the evaluation of binary classifications in our dataset it was possible to identify the main variables(chemical compounds)that discriminate between the countries.We used the following algorithms:Synthetic Minority over-sample Technique and under-sampling to balance the dataset of each classification approach,the Relief algorithm to obtain a variable importance ranking and the classifiers Support Vector Machines,Multilayer Perceptron and Radial Basis Function Network with dynamic decay adjustment.SVM model obtained the highest performance measures among the classifiers for each dataset(93.73%of accuracy for the Brazil versus non-Brazil,91.18%for the Argentina versus non-Argentina,79.16%for the Chile versus non-Chile,and 91.67%for the Uruguay versus non-Uruguay classification).These accuracies were achieved by the search of the possible variable subsets according to Relief for each classification approach.We found that some variables,such as DPPH,wine color and individual anthocyanins,are among the most important variables in the characterization of Merlot wines.