LAUSR

Technology development in satellite, sensor network and image processing has been applied for various issues. Such developments are well adapted towards smart agriculture in recent times. It has been adapted to monitor the plants for their growth and yield being achieved. There exist different approaches towards monitoring the plants in agriculture sector, but the issue is with the performance metrics which are not arrived up to expected level. To improve the performance in plant monitoring with security concerns, an efficient multi feature multi feature plant management framework is proposed in this paper. The proposed model utilizes different images of statellite over the agriculture region and local images obtained with regional data sets. The images obtained are extracted for the features of texture, color to classify the images against different diseases and deficiencies. Also, with the color features and temperature, fluid, rainfall features, the method computes yield aggregation weight, Growth Aggregation Weight towards regulation of fluid. Using such weight measures estimated, the approach would perform sanitization as well as fertilizer manaagement. Also, the transmission of control messages are adapted to control the fertilizer injectors, water regulators and so on. The model enables controlling each device on the field with the support of sensor nodes. The entire data placed in the channel are encrypted using Feature level Data Encryption which uses different encryption keys and standards for different attributes. This restricts the sensor nodes in decrypting only allowed features to perform controlling the devices under the node and to perform required actions. The proposed method improves the security in smart agriculture and improves the performance.