Early season detection of rice plants using RGB, NIRGB and multispectral images from unmanned aerial vehicle (UAV) Abstract   Crop plant detection is vital for mapping crop planting area and extracting pure crop canopy information. In this study, three cameras (RGB, color infrared (NIRGB) and multispectral (MS) camera) were mounted on a multirotor unmanned aerial vehicle (UAV) to obtain images of rice canopy at the early growth stages (tillering, jointing and initial booting stages). We proposed a new decision tree (DT) combining texture features (mean and variance (C.V)) and spectral features (TSDT) for rice plants detection within UAV images. First, the image was classified into the pure class and the mixed class based on the C.V value. Then the pure class was classified into rice plants and road by the DN or reflectance value in red band. The mixed class was classified into rice plants and background (soil, water and duckweed) through comparing each pixel value to the mean value within the moving window. The results showed that TSDT exhibited an averaged high classification accuracy with overall accuracy (OA) and kappa coefficient (KC) of 91.25%, 0.86, 92.88%, 0.86 and 93.53%, 0.88 for RGB, NIR and MS image among the early three growth stages, respectively. The highest estimation accuracy was obtained at booting stage and the lowest was at tillering stage. Compared with the traditional classification methods, the TSDT method achieved an improved estimation accuracy of 5.2xe2x80x9326.71% in OA and 0.06xe2x80x930.40 in KC. Therefore, this TSDT method is a reliable approach for crop plants detection using UAV imagery.

Deep localization model for intrarow crop detection in paddy field Abstract   Automated and precise rice plant localization is crucial for the mechanization of rice production, which can facilitate targeted spraying, sitespecific fertilization, and mechanized weeding etc. Existing approaches adopted thus far have mainly focused on interrow weed detection or rice seedling row detection. Nevertheless, techniques for intrarow individual rice plant positioning continue to face major challenges induced by the specific paddy field environments or complex morphology of rice plant. This paper proposed a new deep localization network for intrarow rice detection at the single plant level in a paddy field. This method designed a twostage model. The module in stage 1 identified potential locations containing rice plants in the entire image. The module in stage 2 predicted the confidence of rice plant identification and refined the corresponding box bounds. The twostage processing modules shared a deep backbone network for learning fullimage convolutional features and are combined into a unified framework to facilitate an endtoend training. In addition, we constructed a rice plant detection dataset and proposed a taskoriented evaluation method for performance verification of the algorithm. Experiment results showed the proposed deep model achieved a high localization accuracy of 93.22% and a high testing speed of 15 fps, verifying the effectiveness and efficiency of the method. Using this method, we can develop techniques for finerlevel agriculture production, such as spraying and weed control, to achieve healthy and economical rice yields.