Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The problem of remote ed mapping via machine learning is considered.Unmanned aerial vehicles are used to capture maize and sunflor field images.The proposed method considers pattern and feature selection techniques.The final model requires few user information to generalise to new areas.There are features of great influence for the classification of both crops.  approaches the problem of ed mapping for precision agriculture, using imagery provided by Unmanned Aerial Vehicles (UAVs) from sunflor and maize crops. Precision agriculture referred to ed control is mainly based on the design of early post-emergence site-specific control treatments according to ed coverage, where one of the most important challenges is the spectral similarity of crop and ed pixels in early growth stages. Our work tackles this problem in the context of object-based image analysis (OBIA) by means of supervised machine learning methods combined with pattern and feature selection techniques, devising a strategy for alleviating the user intervention in the system while not compromising the accuracy. This work firstly proposes a method for choosing a set of training patterns via clustering techniques so as to consider a representative set of the whole field data spectrum for the classification method. Furthermore, a feature selection method is used to obtain the best discriminating features from a set of several statistics and measures of different nature. Results from this research show that the proposed method for pattern selection is suitable and leads to the construction of robust sets of data. The exploitation of different statistical, spatial and texture metrics represents a new avenue with huge potential for beten and within crop-row ed mapping via UAV-imagery and shows good synergy when complemented with OBIA. Finally, there are some measures (specially those linked to vegetation indexes) that are of great influence for ed mapping in both sunflor and maize crops.
['María Pérez-Ortiz', 'José Manuel Peña', 'Pedro Antonio Gutiérrez', 'Jorge Torres-Sánchez', 'César Hervás-Martínez', 'F. López-Granados']
I am writing a research paper on a idea Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery
[{"from": "human", "value": "I am writing a research paper on a idea Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The problem of remote ed mapping via machine learning is considered.Unmanned aerial vehicles are used to capture maize and sunflor field images.The proposed method considers pattern and feature selection techniques.The final model requires few user information to generalise to new areas.There are features of great influence for the classification of both crops.  approaches the problem of ed mapping for precision agriculture, using imagery provided by Unmanned Aerial Vehicles (UAVs) from sunflor and maize crops. Precision agriculture referred to ed control is mainly based on the design of early post-emergence site-specific control treatments according to ed coverage, where one of the most important challenges is the spectral similarity of crop and ed pixels in early growth stages. Our work tackles this problem in the context of object-based image analysis (OBIA) by means of supervised machine learning methods combined with pattern and feature selection techniques, devising a strategy for alleviating the user intervention in the system while not compromising the accuracy. This work firstly proposes a method for choosing a set of training patterns via clustering techniques so as to consider a representative set of the whole field data spectrum for the classification method. Furthermore, a feature selection method is used to obtain the best discriminating features from a set of several statistics and measures of different nature. Results from this research show that the proposed method for pattern selection is suitable and leads to the construction of robust sets of data. The exploitation of different statistical, spatial and texture metrics represents a new avenue with huge potential for beten and within crop-row ed mapping via UAV-imagery and shows good synergy when complemented with OBIA. Finally, there are some measures (specially those linked to vegetation indexes) that are of great influence for ed mapping in both sunflor and maize crops."}]