Now showing 1 - 2 of 2
  • Publication
    Extracting Pasture Phenotype and Biomass Percentages using Weakly Supervised Multi-target Deep Learning on a Small Dataset
    The dairy industry uses clover and grass as fodder for cows. Accurate estimation of grass and clover biomass yield enables smart decisions in optimizing fertilization and seeding density, resulting in increased productivity and positive environmental impact. Grass and clover are usually planted together, since clover is a nitrogen-fixing plant that brings nutrients to the soil. Adjusting the right percentages of clover and grass in a field reduces the need for external fertilization. Existing approaches for estimating the grass-clovercomposition of a field are expensive and time consuming—random samples of the pasture are clipped and then the components are physically separated to weigh and calculate percentages of dry grass, clover and weeds in each sample. There is growing interest in developing novel deep learning based approaches to nondestructively extract pasture phenotype indicators and biomass yield predictions of different plant species from agricultural imagery collected from the field. Providing these indicators and predictions from images alone remains a significant challenge. Heavy occlusions in the dense mixture of grass, clover and weeds make it difficult to estimate each component accurately. Moreover, although supervised deep learning models perform well with large datasets, it is tedious to acquire large and diverse collections of field images with precise ground truth for different biomass yields. In this paper, we demonstrate that applying data augmentation and transfer learning is effective in predicting multi-target biomass percentages of different plant species, even with a small training dataset. The scheme proposed in this paper used a training set of only 261 images and provided predictions of biomass percentages of grass, clover, white clover, red clover, and weeds with mean absolute error (MAE) of 6.77%, 6.92%, 6.21%, 6.89%, and 4.80% respectively. Evaluation and testing were performed on a publicly available dataset provided by the Biomass Prediction Challenge [Skovsen et al., 2019]. These results lay the foundation for our next set of experiments with semi-supervised learning to improve the benchmarks and will further the quest to identify phenotype characteristics from imagery in a non-destructive way.
  • Publication
    An Orthogonal Classification Layer with Kasami Sequences for Discriminative Feature Learning in Neural Networks
    This paper proposes a novel Orthogonal Classification Layer (OCL) utilizing Kasami sequences for neural networks trained for classification problems. OCL consists of a fully connected layer with fixed orthogonal weights and zero biases for all of its output neurons. Attaching the OCL to the end of any neural network encourages the network to generate a unique latent representation (orthogonal code) at its last hidden layer for each data class. This is achieved by associating and fixing the weights for each of the output neurons to a unique code from a set of orthogonal sequences, such as Kasami. When networks use OCL the latent representations they learn for each data class at the end of the network converge to values equivalent to the fixed weights of the OCL. Auto-correlation and cross-correlation properties of orthogonal codes maximize the output of the correct class and increase its separation from the outputs of all other classes. Therefore, networks trained with OCL benefit from a wider classification decision margin than networks without OCL. Moreover, the feature sets extracted by the network for different data classes are well separated and more amenable to human interpretation. The implementation of OCL is simple and OCL can easily be integrated into any neural network architecture without a need for network architecture modifications or changes to the training scheme (for example optimization, normalization, or regularization) adopted in the original network architecture. The computational and memory cost required for the OCL is lower than conventional classification layers since the weights are fixed and no gradient is required. Though simple and practical the proposed OCL enhances learning of discriminative latent representations, generates explainable features, and provides high classification accuracy. We demonstrate this through a set of evaluation experiments comparing the performance of equivalent networks with and without OCL.