Water is the main resource for agriculture. Management of water in agricultural field is a challenging process. To manage the water content in the agricultural field, smart irrigation system has been proposed by using fuzzy based decision support system on Hyperspectral Image benchmark dataset. Hyperspectral images are the process of collected and processed the images from electromagnetic spectrum. Recent studies show that hyperspectral images are very accurate in collecting the soil moistures value. Dataset is collected in five-day field of campaign the soil is the type of clayey slit and it is non vegetation. Hyperspectral datasets which consist of range value between 454 to 598 nm. Value is gathered from the 285 hyperspectral snapshot camera recording images with 125 spectral bands with the spectral resolution of 4 nm. Experimental results of this method achieve the accuracy of 0.98. Hence the proposed method reduces the water wastage to an extent.

Lung Cancer is one of the most deadly diseases worldwide. According to the American Cancer Society, about 234,030 peoples have been suffering from lung cancer. It can be cured if it is diagnosed earlier which decreases the death rate. A computational diagnostic tool named Computer Aided Diagnosis (CAD) is used to detect pulmonary nodules. Extensive work has been made in this domain. However, previous Computer Aided Diagnosis (CAD) system are time-consuming since they needed more modules such as image modification, segmentation and the features should be extracted by the domain experts to build the entire CAD system. It is hard to examine large data using the existing CAD system. Thus, a novel framework with a Convolutional Neural Network (CNN) to detect pulmonary nodule is proposed. Firstly, a preprocessing technique named bilateral filtering is applied to increase the image quality and remove the irrelevant noise from the Computer Tomography (CT) images. Secondly, the preprocessed data are trained into a convolutional neural network to detect the nodule and classify it. The performance of this system is validated using the Lung Image Database Consortium (LIDC) dataset. The accuracy of nodule candidate detection achieves 93%. It states that the proposed method achieves better accuracy in nodule detection.