DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning

In this paper, we investigate estimating emergence and biomass traits from color images and elevation maps of wheat field plots. We employ a state-of-the-art deconvolutional network for segmentation and convolutional architectures, with residual and Inception-like layers, to estimate traits via high dimensional nonlinear regression. Evaluation was performed on two different species of wheat, grown in field plots for an experimental plant breeding study. Our framework achieves satisfactory performance with mean and standard deviation of absolute difference of 1.05 and 1.40 counts for emergence and 1.45 and 2.05 for biomass estimation. Our results for counting wheat plants from field images are better than the accuracy reported for the similar, but arguably less difficult, task of counting leaves from indoor images of rosette plants. Our results for biomass estimation, even with a very small dataset, improve upon all previously proposed approaches in the literature.Comment: WACV 2018 (Code repository: https://github.com/p2irc/deepwheat_WACV-2018

A Segmentation-Guided Deep Learning Framework for Leaf Counting

Deep learning-based methods have recently provided a means to rapidly and effectively extract various plant traits due to their powerful ability to depict a plant image across a variety of species and growth conditions. In this study, we focus on dealing with two fundamental tasks in plant phenotyping, i.e., plant segmentation and leaf counting, and propose a two-steam deep learning framework for segmenting plants and counting leaves with various size and shape from two-dimensional plant images. In the first stream, a multi-scale segmentation model using spatial pyramid is developed to extract leaves with different size and shape, where the fine-grained details of leaves are captured using deep feature extractor. In the second stream, a regression counting model is proposed to estimate the number of leaves without any pre-detection, where an auxiliary binary mask from segmentation stream is introduced to enhance the counting performance by effectively alleviating the influence of complex background. Extensive pot experiments are conducted CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. The experimental results demonstrate that the proposed framework achieves a promising performance both in plant segmentation and leaf counting, providing a reference for the automatic analysis of plant phenotypes

Object Counting with Deep Learning

This thesis explores various empirical aspects of deep learning or convolutional network based models for efficient object counting. First, we train moderately large convolutional networks on comparatively smaller datasets containing few hundred samples from scratch with conventional image processing based data augmentation. Then, we extend this approach for unconstrained, outdoor images using more advanced architectural concepts. Additionally, we propose an efficient, randomized data augmentation strategy based on sub-regional pixel distribution for low-resolution images. Next, the effectiveness of depth-to-space shuffling of feature elements for efficient segmentation is investigated for simpler problems like binary segmentation -- often required in the counting framework. This depth-to-space operation violates the basic assumption of encoder-decoder type of segmentation architectures. Consequently, it helps to train the encoder model as a sparsely connected graph. Nonetheless, we have found comparable accuracy to that of the standard encoder-decoder architectures with our depth-to-space models. After that, the subtleties regarding the lack of localization information in the conventional scalar count loss for one-look models are illustrated. At this point, without using additional annotations, a possible solution is proposed based on the regulation of a network-generated heatmap in the form of a weak, subsidiary loss. The models trained with this auxiliary loss alongside the conventional loss perform much better compared to their baseline counterparts, both qualitatively and quantitatively. Lastly, the intricacies of tiled prediction for high-resolution images are studied in detail, and a simple and effective trick of eliminating the normalization factor in an existing computational block is demonstrated. All of the approaches employed here are thoroughly benchmarked across multiple heterogeneous datasets for object counting against previous, state-of-the-art approaches

A segmentation-guided deep learning framework for leaf counting

Deep learning-based methods have recently provided a means to rapidly and effectively extract various plant traits due to their powerful ability to depict a plant image across a variety of species and growth conditions. In this paper, we focus on dealing with two fundamental tasks in plant phenotyping, i.e., plant segmentation and leaf counting, and propose a two-steam deep learning framework for segmenting plants and counting leaves with various size and shape from two-dimensional plant images. In the first stream, a multi-scale segmentation model using spatial pyramid is developed to extract leaves with different size and shape, where the fine-grained details of leaves are captured using deep feature extractor. In the second stream, a regression counting model is proposed to estimate the number of leaves without any pre-detection, where an auxiliary binary mask from segmentation stream is introduced to enhance the counting performance by effectively alleviating the influence of complex background. Extensive pot experiments are conducted on the CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. Experimental results demonstrate that the proposed framework achieves a promising performance both in plant segmentation and leaf counting, providing a reference for the automatic analysis of plant phenotypes