REAL-TIME SELECTIVE SPRAYING FOR VIOLA ROPE CONTROL IN SOYBEAN AND COTTON CROPS USING DEEP LEARNING

ABSTRACT

The cultivation of soy and cotton is of great importance in the Brazilian economic scenario, both of which move billions of reais per year in exports. Weed management is important for obtaining optimal yields. Among the plants that have gained resistance and tolerance are those of the genus Ipomoea spp. These plants affect soybean and cotton crops throughout their cycle, thereby affecting their productivity. In this context, the objective of this work was to develop an embedded system for the selective spraying of rope and viola in cotton and soybean crops using algorithms for the classification and detection of objects in real time (Faster R-CNN and YOLOv3). This project was developed at the Agricultural Machinery Laboratory of the Federal University of Rondonópolis. The algorithms were trained to detect three classes (soybean, viola, and cotton) and were evaluated in terms of precision and sensitivity in the laboratory and field. Control results using faster R-CNN sprays demonstrated that real-time object detection algorithms for the selective control of weeds can be used for soybean and cotton crops.

KEYWORDS
Artificial intelligence; machine learning; spray; convolutional neural networks