One of the bases for implementing Integrated Pest Management (IPM) programs is the determination of pest population levels in the field, for later making decisions about controlling such pests, based on known action thresholds. However, pest control in Brazilian soybean fields has not been done based on IPM precepts, but instead, based on "calendar" spraying, which harms the ecosystem's balance. This happens mostly because of the particular characteristics of soybean production in Brazil, such as large fields, succession with crops that share the same pest species, large use of fungicides, previous sales of production and shorter crop cycles. Aiming at recovering the system's sustainability, this study proposes the use of Remote Sensing techniques to optimize pest sampling in soybean fields, providing subsidy for growers to adopt IPM practices, therefore using control methods only when and where pest populations reach action thresholds. To do so, semi-field studies will be carried, with different infestation levels of economically important pest species, being two defoliator caterpillars (Spodoptera eridania and Chrysodeixis includens) and two stink bugs (Dichelops melachantus and Euschistus heros). Infested plants will be monitored for their reflectance using a hyperspectral imaging sensor. Furthermore, field studies will be carried out, where the natural infestation of different species might happen, using a multispectral imaging sensor attached to aa unmanned aerial vehicle. Besides, field samplings will be done in loco, and infested plants will be taken to the lab for reflectance analysis, using the hyperespectral sensor. We expect to correlate the infestation levels observed in the field with multi and hyperspectral responses, to create a base of information to be used for sampling soybean pests in other regions of Brazil, by using remote sensing tools.
News published in Agência FAPESP Newsletter about the scholarship: