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Ignazio Gallo, Riccardo La Grassa, Nicola Landro and Mirco Boschetti
In this paper, we provide an innovative contribution in the research domain dedicated to crop mapping by exploiting the of Sentinel-2 satellite images time series, with the specific aim to extract information on ?where and when? crops are grown. The fina...
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Yaoqiang Pan, Xvlin Xiao, Kewei Hu, Hanwen Kang, Yangwen Jin, Yan Chen and Xiangjun Zou
In an unmanned orchard, various tasks such as seeding, irrigation, health monitoring, and harvesting of crops are carried out by unmanned vehicles. These vehicles need to be able to distinguish which objects are fruit trees and which are not, rather than...
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Lixue Zhu, Zhihao Zhang, Guichao Lin, Pinlan Chen, Xiaomin Li and Shiang Zhang
Currently, the detection and localization of tea buds within the unstructured tea plantation environment are greatly challenged due to their small size, significant morphological and growth height variations, and dense spatial distribution. To solve this...
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Jesús Dassaef López-Barrios, Jesús Arturo Escobedo Cabello, Alfonso Gómez-Espinosa and Luis-Enrique Montoya-Cavero
In this paper, a mask region-based convolutional neural network (Mask R-CNN) is used to improve the performance of machine vision in the challenging task of detecting peduncles and fruits of green sweet peppers (Capsicum annuum L.) in greenhouses. One of...
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Yuan Li, Mayire Ibrayim and Askar Hamdulla
In the last years, methods for detecting text in real scenes have made significant progress with an increase in neural networks. However, due to the limitation of the receptive field of the central nervous system and the simple representation of text by ...
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