Smart Farming Using Deep Learning

Abstract

The agricultural sector plays important role in supplying quality food and makes the greatest contribution to growing economies and populations. Agriculture is extremely important in human life. Almost 60% of the population is engaged in some kind of agriculture either directly or indirectly. Plant disease may cause significant losses in food production and eradicate diversity in species. Early of plant disease using accurate of automatic detection techniques can enhance the quality of food production and minimize economic losses. In recent years, deep learning has brought tremendous improvements in the recognition accuracy of image classification and object detection systems. Hence, in this paper, we utilized convolutional neural network (CNN)-based pre-trained models for efficient plant disease detection. The proposed method uses a convolutional neural network and deep neural network to identify and recognize crop disease symptoms effectively and accurately. This Research paper offers a through description of the DL models that are used to visualize crop diseases. In this experiment we use plant dataset, which has real time image samples of different plant, fruit, flower in different. The proposed methodology aims to develop a convolution neural network-Based strategy for detecting plant leaf, fruit, flower disease.