Detection of surface properties using image recognition techniques using deep learning algorithms

The era of Artificial Intelligence has achieved great advancements in the field of robotics. Deep convolutional neural networks which are branch in artificial intelligence have succeeded in solving many computer vision problems. Therefore, we chose to use ConvNets in detecting vegetation types and the state of the vegetation according to the nutrition content. To implement this, we have approached multi-task learning, where the same model is used to detect the type of the vegetation first, followed by the detection of the nutrition level. We have designed multiple architectures and finally used modified VGGNet model in classifying the nutrition level and custom architecture in classifying the type of the vegetation. As a pioneer in implementing the task using ConvNets, we have created our own dataset. Two patches with vegetation are planted and the nutrition for one patch is not provided while for the second patch regular nutrition is implemented. Images are extracted from both of the patches at regular intervals and are divided into different classes at every consecutive week after restricting the nutrition. The data is divided into 5 classes with 2000 images in each class. These five classes are divided according to the state of the vegetation without nutrition after every consecutive week. In this work, the possibilities to improve the accuracy considering time and resources into account are investigated and discussed. We have compared the obtained results using different architectures with different hyper-parameters.