DETECTING PLANT DISEASES USING DEEP LEARNING ARCHITECTURES

The technological advancements in artificial intelligence enabled the usage of complex mathematical models in various fields, such as agriculture. Not long ago, the only method of detecting certain abnormalities in the development of a plant was the trained eye of an expert. Today, we can make use of deep learning techniques to precisely identify these problems. For a multitude of crops, the abnormalities in the development of the plant can be identified by carefully analyzing the health of the leaves. In this paper, we make use of a public leaf disease dataset to identify 2 different diseases commonly found on potato leaves, namely the early blight and the late blight. Using GoogLeNet's Inception-v3 convolutional neural network (CNN) architecture, we trained the model using a total of 6456 images, of which 456 were depicting healthy leaves. 5166 images were used for training, while 1290 were used for validation. The network was trained 3 times, using fully colored, grayscale and segmented images. The achieved overall accuracy range between 93.48% for grayscale images and 97.67% for segmented images.