Detection of nematodes in soybean crop by drone

Abstract

ABSTRACT Global consumption of oilseeds has been growing progressively in the last five growing seasons, in which soybean represents 60% of this sector. Thus, in order to maintain a high production in the region of Rio Verde, State of Goiás, against the phytopathological problems, this study aimed to define the best spectral range for the detection of H. glycines and P. brachyurus by linear regressions in soybean at R3 stage, as well as the elaboration of mathematical models through multiple linear regressions. For this, soil and root were sampled in the experimental area, as well as a flight was performed with the Sentera sensor. Data were used for the elaboration of regressions and for the validation of 2 mathematical models. Significant values were observed in simple linear regression only for cysts, in the visible range, with a good R² value for the Green, Red and 568 nm bands, to nonviable cysts. When working with the stepwise statistics, better results are found for H. glycines, which now has an R²(aj) of 0.7430 and P. brachyurus is then detected. From the mathematical model obtained with the multiple linear regression for non-viable cysts with an R²(aj) of 0.7430, it is possible to detect the spatial distribution of nematodes across the soybean field, in order to perform a localized management, optimizing the applications. Good results are also possible using the mathematical model obtained by simple linear regression.

Keywords

• Remote sensing
• Image processing
• Heterodera glycines
• Pratylenchus brachyurus
• Digital Farming