Agriculture has advanced tremendously over the last 100 years. In fact it is been keeping up with food production at a very high rate. In fact, some scientists feel that agriculture already produces enough food to feed the world, but of course there are issues and problems with food availability, agricultural production practices, preservation and transportation, and probably more that one can think of that hinder many people in this world from getting adequate food. The basic challenge is to provide food for the needy people. This need can be fulfilled with the help of the farmers taking responsibility in increasing the food production by 50% by the year 2050. The objective of the present work is to increase this food production, protecting the environmentwith managing natural resources. Mainly focusing on water, nutrients and other inputs to produce foods without degrading the environment. The Goal is to develop the social, environmental, and the economic aspects of possible solutions to minimize the agricultural footprint, and become more sustainable. The dataset considered in our experiment is used in yield prediction based on historic yield and weather information. Implemented both the versions of Thomson model and compared the result with segmentation model, Random Forest (RF). Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are used as evaluation metrics in estimating the performance of models implements and stated that Random forest algorithm is providing 0.07 (RMSE). The outcome of the present research work helps farmers in adopting best management practices and trying to give them the economical and technical support in making easier for them to adopt best management practices.

This paper proposes Computerized Aided Detection System (CAD) which uses Content Based Image Retrieval (CBIR) to detect cancer nodules present in an image. The CAD system is concerned for the radiologists to identify lung cancer at premature stages, which are very tiny nodules that are not able to seen by naked eye. In modern years, Image processing techniques play a key role in predicting diseases at early stages in particular in various cancer types such as liver cancer, breast cancer etc. This paper comprises of four steps: i) preprocessing an image in order to lessen the noise level and the accuracy of the image is to be improved, so that the accuracy in detection will be higher. ii) The image is segmented based on Marker-Controlled Watershed Segmentation. iii) The features of the nodules present in the image are extracted using GLCM. iv) The nodules are classified based on the extracted features using KNN classifier. The Content Based Image Retrieval Technique is used which is used to redeem query based images in the database by combining feature extraction and similarity matching methods. For experimentation of proposed technique, CT images are used which are extracted from Lung Image Database Consortium database (LIDC).

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