EARLY DISEASE DETECTION IN TOMATO PLANTS USING NEURAL NETWORK

EARLY DISEASE DETECTION IN TOMATO PLANTS USING NEURAL NETWORK

ABSTRACT

Agriculture is one field which has a high impact on life and economic status of human beings. Improper management leads to loss in agricultural products. This process is to detect plant disease using the deep neural network, the alternative of conventional neural networks. This can easily detect the disease of plant leaves. First user can upload the image of the plant and it will upload the image on the screen. Then analyse the image by pressing the button. The disease can analyse and show the status of a plant that is healthy or unhealthy. The disease can be detected in the image of plant leaves.  Due to the improvement and development in technology where devices are smart enough to recognize and detect plant diseases. Recognizing illness can prompt faster treatment in order to lessen the negative impacts on harvest. This process focuses upon plant disease detection using an image processing approach. This work utilizes an open dataset of 5000 pictures of unhealthy and solid plants, where convolution systems and semi-supervised techniques are used to characterize crop species and detect the sickness status of 4 distinct classes. The convolutional neural network algorithm is used to train the images of the plant and detect the disease from the image. Then it will predict the detection model of the plant leaf images in the form of accuracy.