A Curriculum Learning Approach to Classify Nitrogen Concentration in Greenhouse Basil Plants Using a Very Small Dataset and Low-cost RGB Images

The automatic classification of plants with nutrient deficiencies or excesses is essential in precision agriculture. In particular, being able to perform early detection of nutrient concentrations would increase the production of crop yields and make appropriate use of fertilizers. RGB cameras represent a low-cost alternative sensor for plant monitoring, but this task is complicated when it is purely visual and has limited samples. In this paper, we analyze the Curriculum by Smoothing technique with a small dataset of RGB images (144 images per class) to classify nitrogen concentrations in greenhouse basil plants. Our Deep Learning method changes the texture information found in the images during training by convolving each feature map (the output of a convolutional layer) with a Gaussian kernel whose width increases as training progresses. We observed that controlled information extraction allows a state-of-the-art deep neural network to perform well using little training data containing a high variance between items of the same class. As a result, the Curriculum by Smoothing provides an average accuracy 7% higher than the traditional transfer learning method for the classification of the nitrogen concentration level of greenhouse basil 'Nufar' plants with little data.