Papers by Badri Narayanan
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
8 fold upsampling, deblurring and visual domain transfer with a large amount of unlabeled drone i... more 8 fold upsampling, deblurring and visual domain transfer with a large amount of unlabeled drone images. We validate our results on a small held-out drone image test set to show the validity of our approach, which opens the way for automated dry herbage biomass monitoring www.github. com/PaulAlbert31/Clover_SSL.
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021
Monitoring species-specific dry herbage biomass is an important aspect of pasture-based milk prod... more Monitoring species-specific dry herbage biomass is an important aspect of pasture-based milk production systems. Being aware of the herbage biomass in the field enables farmers to manage surpluses and deficits in herbage supply, as well as using targeted nitrogen fertilization when necessary. Deep learning for computer vision is a powerful tool in this context as it can accurately estimate the dry biomass of a herbage parcel using images of the grass canopy taken using a portable device. However, the performance of deep learning comes at the cost of an extensive, and in this case destructive, data gathering process. Since accurate speciesspecific biomass estimation is labor intensive and destructive for the herbage parcel, we propose in this paper to study low supervision approaches to dry biomass estimation using computer vision. Our contributions include: a synthetic data generation algorithm to generate data for a herbage height aware semantic segmentation task, an automatic process to label data using semantic segmentation maps, and a robust regression network trained to predict dry biomass using approximate biomass labels and a small trusted dataset with gold standard labels. We design our approach on a herbage mass estimation dataset collected in Ireland and also report state-of-the-art results on the publicly released Grass-Clover biomass estimation dataset from Denmark. Our code is available at https://git.io/J0L2a.
Dry biomass weight measurements from a quadrat in a paddock for grass, clover and weeds when expr... more Dry biomass weight measurements from a quadrat in a paddock for grass, clover and weeds when expressed as percentages of total dry herbage mass are compositional in nature. Unlike real valued regression problems, prediction of compositional data is handled differently in statistics because of its closure property where the components of the composition are positive data adding up to a constant sum and is therefore constrained in the simplex space, in our case 100%. Our motivation in this paper was to study whether the adaptation of compositional data analysis (CoDa) techniques in deep learning improves the prediction results over the best performing deep learning model we used in our earlier paper [Narayanan et al., 2021]. Although the log ratio transformation of targets is an appropriate adaptation of CoDa and is interesting for Biomass prediction, our study indicates that the CoDa adaptation does not improve the prediction errors over our earlier method.
Irish Machine Vision and Image Processing Conference, 2020
The dairy industry uses clover and grass as fodder for cows. Accurate estimation of grass and clo... more 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-clover composition 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.
Artificial Intelligence and Cognitive Science, 2021
Dry biomass weight measurements from a quadrat in a paddock for grass, clover and weeds when expr... more Dry biomass weight measurements from a quadrat in a paddock for grass, clover and weeds when expressed as percentages of total dry herbage mass are compositional in nature. Unlike real valued regression problems, prediction of compositional data is handled differently in statistics because of its closure property where the components of the composition are positive data adding up to a constant sum and is therefore constrained in the simplex space, in our case 100%. Our motivation in this paper was to study whether the adaptation of compositional data analysis (CoDa) techniques in deep learning improves the prediction results over the best performing deep learning model we used in our earlier paper [Narayanan et al., 2021]. Although the log ratio transformation of targets is an appropriate adaptation of CoDa and is interesting for Biomass prediction, our study indicates that the CoDa adaptation does not improve the prediction errors over our earlier method.
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Papers by Badri Narayanan