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Texture pattern of finger back surface is highly unique consisting of creases and lines which can be used for biometric authentication system. Use of Finger Knuckle Print (FKP) for person identification has been attracted attention of researchers in last few years .Finger Knuckle Print is becoming emerging biometric identifier. In this paper, we present a finger knuckle identification method that uses Dynamic programming (DP) for the alignment of Radon Like Features. The key idea is to use dynamic time warping (DTW) to match Radon Like features of two knuckle images. Experiment is carried out using IIT Delhi finger knuckle database version 1.0. Knuckle features are extracted using the Radon Like Feature technique is classified using DTW for the identification of finger knuckle print. Result obtained using RLF and DTW is promising as compared to subspace and Gabor filtering methods.

Automation is in great demand in the field of agriculture science, to check quality of fruits, vegetables and crops. Automation helps to increase quality of fruits, vegetables and crops results in economic growth and productivity of the country. Classifying fruits or vegetables into different grades helps to evaluate cost based on grading. Automatic quality evaluation is in great demand in the export market. Cost of fruits, the consumer's preference and choice are greatly depend on appearance of fruits and vegetables. In India mostly the sorting and grading is being done manually by human being but it is not consistent. It is also very time consuming process. In addition to this it is expensive and grading decision may vary by surrounding influence. Therefore an automated fruit grading system is required to grade the fruits. This paper proposing fruit grading system using fruit image processing and classification. Using fruit classification grades of fruits can be determined easily. Fruit grading is based on attribute of fruits such as size, color, texture, shape and defects or disease of fruit.

Agriculture is the most important sector in the Indian Economy and gives contribution in the form of agricultural productivity. To increase the agricultural productivity, precise and on-time detection of crop diseases and pest is needed. Most of the times, farmers fail to take the necessary steps even if they may have been able to identify the problem. Moreover, in some rural areas farmers cannot get rid of these problems because they do not have proper knowledge or education on how to do so. Most of the cases involve leaf diseases which are not recognized properly, and farmers end up using insecticides which may not be suitable for that particular disease. This paper provide new techniques of image pre- processing and a new combination of feature set from the processed images to make a trained model that will show how accurate our methodology is to detect diseases accurately. In this work, Machine learning algorithms namely KNN and SVM are implemented which can detect leaf diseases accurately. Among various plant leaf diseases, Rice leaf disease is one of them. This work is based on three Rice leaf diseases, they are - Bacterial leaf blight, Leaf smut, Brown spot.