Detection of Sugarcane Crop Rows From UAV Images Using Semantic Segmentation and Radon Transform

• Autor(a) principal: Silva, Renato Rodrigues da
• Data de Publicação: 2020
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Institucional da UFU
• Texto Completo: https://repositorio.ufu.br/handle/123456789/31012; http://doi.org/10.14393/ufu.di.2020.736
• topic: Crop-row; Sugarcane; Segmentation; CNN; UAV; Radon Transform; Linhas de Plantio; Cana-de-açúcar; Segmentação; VANT; Transformada de Radon; Computação; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO; Computação

In recent years, UAVs (Unmanned Aerial Vehicles) have become increasingly popular in the agricultural sector, promoting and enabling the application of aerial image monitoring in both scientific and business contexts. Images captured by UAVs are fundamental for precision farming practices, as they allow activities that deal with low and medium altitude images. After the effective sowing, the scenario of the planted area may change drastically over time due to the appearance of erosion, gaps, death and drying of part of the crop, animal interventions, etc. Thus, the process of detecting the crop rows is strongly important for planning the harvest, estimating the use of inputs, control of costs of production, plant stand counts, early correction of sowing failures, more-efficient watering, etc. In addition, the geolocation information of the detected lines allows the use of autonomous machinery and a better application of inputs, reducing financial costs and the aggression to the environment. In this work we address the problem of detection and segmentation of sugarcane crop lines using UAV imagery. First, we experimented an approach based on \ac{GA} associated with Otsu method to produce binarized images. Then, due to some reasons including the recent relevance of Semantic Segmentation in the literature, its levels of abstraction, and the non-feasible results of Otsu associated with \ac{GA}, we proposed a new approach based on \ac{SSN} divided in two steps. First, we use a Convolutional Neural Network (CNN) to automatically segment the images, classifying their regions as crop lines or as non-planted soil. Then, we use the Radon transform to reconstruct and improve the already segmented lines, making them more uniform or grouping fragments of lines and loose plants belonging to the same planting line. We compare our results with segmentation performed manually by experts and the results demonstrate the efficiency and feasibility of our approach to the proposed task.