An effective crop management practice is very important to the sustenance of crop production. With the emergence of Industrial Revolution 4.0 (IR 4.0), precision farming has become the key element in modern agriculture to help farmers in maintaining the sustainability of crop production. Unmanned aerial vehicle (UAV) also known as drone was widely used in agriculture as one of the potential technologies to collect the data and monitor the crop condition. Managing and monitoring the paddy field especially at the bigger scale is one of the biggest challenges for farmers. Traditionally, the paddy field and crop condition are only monitored and observed manually by the farmers which may sometimes lead to inaccurate observation of the plot due the large area. Therefore, this study proposes the application of unmanned aerial vehicles and RGB imagery for monitoring rice crop development and paddy field condition. The integration of UAV with RGB digital camera were used to collect the data in the paddy field. Result shows that the early monitoring of rice crops is important to identify the crop condition. Therefore, with the use of aerial imagery analysis from UAV, it can help to improve rice crop management and eventually is expected to increase rice crop production.
In this study, the performance of Naïve Bayes classification on a high-resolution aerial image captured from a UAV-based remote sensing platform is investigated. K-means clustering of the study area is initially performed to assist in selecting the training pixels for the Naïve Bayes classification. The Naïve Bayes classification is performed using linear and quadratic discriminant analyses and by making use of training set sizes that are varied from 10 through 100 pixels. The results show that the 20 training set size gives the highest overall classification accuracy and Kappa coefficient for both discriminant analysis types. The linear discriminant analysis with 94.44% overall classification accuracy and 0.9395 Kappa coefficient is found higher than the quadratic discriminant analysis with 88.89% overall classification accuracy and 0.875 Kappa coefficient. Further investigations carried out on the producer accuracy and area size of individual classes show that the linear discriminant analysis produces a more realistic classification compared to the quadratic discriminant analysis particularly due to limited homogenous training pixels of certain objects.
The environments such as inaccessible places in forests and deserts obstruct free access to human beings and call for remote access by electronic means to get the geographical nature. This research project deals with the establishment of a system for remotely sensing and gathering the information. Three locations, which reveal the geography in a better manner is decided first and three portable radars are launched into the spots from air. As soon as the radars stabilize their positions they start functioning to scan electronically the environment. Beam sweeping is perfonned for complete coverage of 360 degrees and the action is repeated continuously to get the PPI information of the surroundings. The target information collected at these radar receivers are transferred to satellite link through distributed wireless sensor network. In the remote ground station the information from three radars are processed to extract information about the static and moving o�iects in the spotted area.
Abstract. Machine vision calls for the use of detectors to ascertain the features and type of object portrayed in the image. The employment of unmanned aerial vehicles (UAVs), which can function freely in active and precarious settings, is currently gaining momentum. These vehicles are mainly used for the detecting, classifying and tracking of an object. However, the achievement of these objectives necessitates the involvement of an effective edge detection procedure. Sobel, Canny, Prewitt and LoG are among the many edge detection procedures presently available. In this endeavour, we opted for the utilization of UTeM UAVs images for an evaluation of these edge detection procedures. During our investigations, the ground truth edge images were corroborated by a specialist in this field. The results obtained from these investigations revealed that in terms of accuracy, precision, sensitivity and f-measure, the Prewitt procedure outperforms the other methods mentioned.
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