The cancer-affected area in white blood cell pictures is studied in this paper. AlexNet architecture is used to classify different types of white blood cells. This model, which was trained on cell pictures, first preprocesses the photos before extracting the best feature. The feature is extracted using the convolutional layer, Relu layer, and max pooling layer. For categorization of Eosinophils, Lymphocytes, Monocytes, and Neutrophils, the extracted features are input into a softmax and a completely linked layer. The various phases of image processing are used to improve the quality and accuracy of blood cancer detection. The accuracy of the blood sample image is determined by the affected area.

This paper proposes an AGRIBOT (agricultural robot) designed for agricultural purpose that aids the people to endure so that it achieves to do multi functions such as seedling, turn up the area of land, to put a mass of small drops of liquid on crops, lawn mowing, and to identify the hindrance. This AGRIBOT practices which is back powered by the sun energy. It also consists of a detector which detects the obstacles combining the bluetooth module that is directed by the instructions given to the AGRIBOT. This low cost AGRIBOT is easy to function without requiring personal assistance. It aids the farmers for ease of work to lessen the individual effort, time consumption, and energy. Despite all the obstacles, even day or night agriculture can be performed. This AGRIBOT when compared with all other robots is especially useful because it performs multiple functions that serve as an advanced technique for smart farming.