Vision for Smart Agriculture
Smart agriculture has enormous potential to improve crop and livestock production and reduce impact to the environment. We are exploring sensing algorithms that we believe will play important roles in this.
2022 Innovation of the Year Award
Awarded by the MSU Innovation Center to Madonna Benjamin, Daniel Morris, Steven Yik and Michael Lavagnino for our work on using 3D scanning to advance swine welfare.
Maize Phenotype Estimation from Lidar Scans
Drone-based phenotyping has potential to speed the development of new crop breeds that improve yield and other desired characteristics. In this work we explore how well eleven different phenotypes can be estimated using an UAS-mounted Lidar to scan a maize diversity panel periodically over the growing season. High correlation with manual measurements are obtained with six phenotypes, see our pre-print (2022).
Seed Coat Genetic Analysis
Analyses of the genetics and color characteristics of beans requires careful color extration. Traditionally, this would be done through manual segmentation, but now with the advent of convolutional neural networks, we are able to train a network to segment regions of interest, and so greatly speed up the processing of large numbers of beans. The paper on seed coat color genetics (2021) is a collaboration with plant scientists Rie Sadohara and Karen Cichy.
Pig Detection and Tracking
Computer vision techniques have potential to transform Precision Livestock Farming. Our new methods for semi-supervised deep learning are demonstrating accurate detection and posture estimation of pigs in depth images. Details are in our paper from IROS 2020. Congratulations Steven on paper being a best-paper finalist for Agri-Robotics. Here is an image illustrating the good precision our method achieves with 2-sigma ellipses around estimated joints/features. Click on it to see a video with mesh model reconstructions.
Dry Bean Quality Analysis
Dry beans are an important agricultural product and source of nutrition. Ongoing research is seeking to develop new varieties with improved nutritional properties and resistance to insects, disease and drought, and at the same time maintaining or improving bean quality. An important quality measure is bean intactness after cooking and canning. Splits in the seed coats, such as those below, detract from bean quality.
Our 2019 paper at CVPPP describes a proposed Bean Split Ratio as a measure for quantifying bean quality related to splits, and an automated algorithm for calculating it. If used to select new varieties, this can contribute towards improving bean quality for everyone!
A Pyramid CNN for Dense-Leaves Segmentation
Segmenting leaves in dense foliage is difficult problem. We have made recent progress as shown below. On the left is a an image of dense foliage containing leaves with large internal variations and texture. Our algorithm can automatically estimate boundaries for the individual leaves, as shown on the right.
 |
 |
Dense-Leaves Dataset is now available!
The 2018 paper
and pre-print describing the method are available.Obstacles and Foliage Discrimination for Lidar
Off-road mobile robot navigation can require discrimination of foliage from non-traversable obstacles. We are developing object discrimination technologies that can find navigation hazards, such as tree trunks, rocks, cones etc, that may be partially occluded by foliage. For more details see:Obstacles and Foliage Discrimination for Lidar, D.D. Morris, in proc. SPIE 9837, Unmanned Systems Technology XVIII, 98370E (May 13, 2016); doi:10.1117/12.2224545.
|
|
|
 |
Plant photosynthesis distribution
Experiments on crop breeding, selection and modification require knowledge of photosynthesis rates. Innovative sensor development and processing is needed to to assess photosynthesis and its distribution on plant bodies. .
Growing Depth Image Superpixels for Foliage Modeling, D.D. Morris, S.M. Imran, J. Chen, D.M. Kramer, in proc. Canadian Conf. Computer and Robot Vision, Jun 2016.
Multi-modality Imagery Database for Plant Phenotyping, J. Cruz, X. Yin, X. Liu, S.M. Imran, D.D. Morris, D.M. Kramer, J. Chen, in Journal of Machine Vision Applications., pp. 1-15, November 2015.
