As result of powerful image processing tools, digital image forgeries have already become a serious social problem. In this paper we describe an effective method to detect Copy-Move forgery in digital images. Our technique works by first applying DWT (Discrete Wavelet Transform) to the input image to yield a reduced dimensional representation [1]. Then the compressed image is divided into overlapping blocks. These blocks are then sorted and duplicated blocks are identified using Phase Correlation as similarity criterion. Due to DWT usage, detection is first carried out on lowest level image representation. This approach drastically reduces the time needed for the detection process and increases accuracy of detection process.
India is an agricultural country and this sector accounts for 18 percent of India’s GDP. This sector is the backbone of the country and focuses on better yield by using pesticides and fertilizers to prevent plant disorders which directly affects the yield. The primary method adopted for detecting disorders is through visual observation and other methods are quite expensive. Many authors have proposed solutions to this problem such as IoT for grapes, or system designed for accurate disorder detection using machine learning with limited scope. This paper showcases a prototype that uses multi-modal analysis through sensor data, computer vision. The main objective of this system is to accurately detect disorders in tomato plant using IoT, Machine Learning, Cloud Computing, and Image Processing.
The image segmentation performs a significant role in the field of image processing because of its wide range of applications in the agricultural fields to identify plants diseases by classifying the different diseases. Classification is a technique to classify the plants diseases on different morphological characteristics. Different classifiers are used to classify such as SVM (Support Vector Machine), K-nearest neighbor classifiers, Artificial Neural Networks, Fuzzy Logic, etc. This paper presents different image processing techniques used for the early detection of different Plants diseases by different authors with different techniques. The main focus of our work is on the critical analysis of different plants disease segmentation techniques. The strengths and limitations of different techniques are discussed in the comparative evaluation of current classification techniques. This study also presents several areas of future research in the domain of plants disease segmentation. Our focus is to analyze the best classification techniques and then fuse certain best techniques to overcome the flaws of different techniques, in the future.
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