I am trying to explore computer vision using deep learning techniques. I have gone through basic literature, made a NN of my own to classify digits using MNIST data(without using any library like TF,Keras etc, and in the process understood concepts like loss function, optimization, backward propagation etc), and then also explored Fashion MNIST using TF Keras.
I applied my knowledge gained so far to solve a Kaggle problem(identifying a plant type), but results are not very encouraging.
So, what should be my next step in progress? What should I do to improve my knowledge and models to solve more complex problems? What more books, literature etc should I read to move ahead of beginner stage?
You should try hyperparameter tuning, it will help improve your model performance. Feel free to surf around various articles, fine tuning your model will be the next step as you have fundamental knowledge regarding how model works.