In this paper, the problem of Feature-Based Image Registration (FBIR) has been addressed using a novel hybrid feature detector. To prove the efficiency of the proposed hybrid feature detector, its performance is compared with that of the state-of-the-art feature detectors like BRISK, FAST, ORB, Harris, MinEigen and MSER. Three different image acquisition methods are choosen for comparison. They are (i) Rotation transformation (ii) Scene to model transformation and (iii) Scaling transformation. Taking into consideration the above three types of image acquisition methods, a FBIR method is proposed for different types of remote sensing images. This paper proposed a hybrid feature detection algorithm, that consumes less time to detect the feature key points and it is also rotation and scale invariant. In addition to this the proposed algorithm performs well in terms of match points and match rate. This paper also focuses on a comparative analysis of BRISK, FAST, Harris, MinEigen, MSER and ORB with the proposed algorithm in terms of time to detect feature keypoints. It can be observed from the results and tables that in the case of hybrid feature detectors,It takes less time to detect the feature key points.

Basically, it is hard for endeavors to recognize plant leaf images by a layman due to the varieties in some plant leaves and the extensive information collected for investigation. It is hard to build an automated recognition framework that can handle massive data and give an intermediate analysis. Image examination and order and pattern recognition are some issues that are effectively connected to the existing methods. This paper focuses on designing an automated plant recognition system based on the best recognition algorithm and the Google platform to locate all plant locations on a map. A case study of India, which has huge biodiversity, is illustrated. The proposed system can show the detailed location of that particular species, where they can be found, and the shortest distance from the current location.

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