Graduate students from Mississippi State University's academic departments are using data science to address important human challenges in many domains.
Class-aware Fish Species Recognition Using Deep Learning for an Imbalanced Dataset
Fish species recognition is crucial to identifying the abundance of fish species in a specific area, controlling production management, and monitoring the ecosystem, especially identifying the endangered species, which makes accurate fish species recognition essential. In this work, the fish species recognition problem is formulated as an object detection model to handle multiple fish in a single image, which is challenging to classify using a simple classification network. The proposed model consists of MobileNetv3-large and VGG16 backbone networks and an SSD detection head. Moreover, a class-aware loss function is proposed to solve the class imbalance problem of our dataset. The class-aware loss takes the number of instances in each species into account and gives more weight to those species with a smaller number of instances. This loss function can be applied to any classification or object detection task with an imbalanced dataset. The experimental result on the large-scale reef fish dataset, SEAMAPD21, shows that the class-aware loss improves the model over the original loss by up to 79.7%. The experimental result on the Pascal VOC dataset also shows the model outperforms the original SSD object detection model.
Student Scientist: Simegnew Alaba
Advisor: Dr. John Ball Responsible Unit: CAVS
Department: Electrical and Computer Engineering This research was funded by HPC2-NGI.
Project Information: Research Paper in Sensors 2022, 22(21)
Deep Learning-based Global Soil Moisture Estimation Using CYGNSS Delay Doppler Maps
Remote sensing-based soil moisture estimation has become much popular nowadays. Various satellite missions have been launched to retrieve soil moisture from the earth's surface. But the main challenge is to get soil moisture at higher spatial and temporal resolution. However, Global Navigation Satellite System (GNSS) signals at the L-band frequency can reflect off the land surface and deliver high-resolution land surface information, including surface soil moisture. Cyclone Global Navigation Satellite System (CYGNSS) constellation generates Delay-Doppler Maps (DDM)s, containing important earth surface information from GNSS reflection measurements. CYGNSS is intended to measure the ocean surface wind field with unprecedented temporal and spatial resolution, under all precipitating conditions and across the entire dynamic range of wind speeds experienced in a tropical cyclone. Mississippi State researchers from the Information Processing and Sensing (IMPRESS) lab utilized this satellite mission and proposed a new deep-learning algorithm to determine complex relationships between the reflected measurements and surface parameters which can provide improved soil moisture estimation. The model is trained and validated using the Soil Moisture Active Passive (SMAP) mission's enhanced SM products. The developed model is able to provide global surface soil moisture products at a daily 9 km resolution.
Developing a Pollen Nutrition Database for North America: Healthy Food for Healthy Bees
Poor nutrition is one of the major stressors of bee species and is a main contributor to loss in pollinator populations. Bees are currently faced with many nutritional challenges, including loss of forage habitat and monoculture. The aim of this project is to promote better nutrition for bees by learning which floral resources are nutritionally optimal for all bees based on the nutritional composition of their pollens. This study will first use various methods of pollen collection in order to collect sufficient pollen from each target species of plants. Next, the collected pollen samples will be analyzed in the lab using basic biochemical assays, as well as mass spectrometry-based methods, to determine the nutritional quality of the pollen. The pollen will be analyzed for its concentration of proteins, lipids, amino acids, sterols, metabolites, and phytochemicals. Finally, all of this data collected will be compiled into an online database showcasing the nutritional quality of each plant species’ pollen. This database can be used by beekeepers, conservation groups, researchers, growers, and policymakers to scientifically select forage plants for bee pollinators.
Student Scientist: Lauren Jennings
Advisor: Dr. Priyadarshini Chakrabarti Basu
Responsible Unit: PCB Lab, Funded by the United States Department of Agriculture AFRI
Department: Biochemistry, Molecular Biology, Entomology, and Plant Pathology
Autonomous Robot Coverage Path Planning and Mapping
In our daily life and varying society, some application scenarios using autonomous robots include vacuum robots, painter robots, demining robots, lawn mowers, unmanned harvesters, and window cleaners, which require complete coverage path planning (CPP) for robot navigation. Such scenarios typically necessitate completely covering of the entire workspace along the robot's running trajectory while optimizing travel distance, energy consumption, and time. CPP methods with its great research significance, due to its technological hurdles, have been developed in this project. CPP is extensively employed in precision agriculture worldwide. In the event of dynamic changes in the environment, however, practical implementations may confront computational complexity issues. Therefore, two different CPP frameworks are developed for indoor and outdoor settings of precision agriculture.
In outdoor settings, such as weeding and harvesting on farms. A three-layer framework is proposed: the first layer is based on the satellite map to CPP considering the shape of the workspace; the second layer is utilized to plan a collision-free path in light of the obtained onsite images by UAVs; the third layer is followed to avoid unknown and dynamic obstacles through onboard LiDAR sensors. In order to lessen computational cost, the proposed framework progresses more precisely layer by layer depending on the obtained environmental data.
In indoor settings with limited sensing capabilities, such as poultry barns, autonomous robots are required to eliminate broiler mortality. Two robots are developed using a multi-layered CPP system to detect and remove broiler mortality. One detection robot is utilized to search entire broiler barns and detect dead birds. The combination of informative path planning and CPP enables a more efficient search of the entire broiler barn based on the historical data on the distribution of dead birds. The dead bird detector is embedded to detect and indicate the locations of dead broilers. The other removal robot moves directly to the corresponding dead broiler position in an optimized order to clear the dead birds. The results of simulation, comparative studies and experiments demonstrate the effectiveness and the remarkable performance of the proposed framework.
Lei T, Luo C, Jan GE and Bi Z (2022) Deep Learning-Based Complete Coverage Path Planning With Re-Joint and Obstacle Fusion Paradigm. Front. Robot. AI 9:843816. doi: 10.3389/frobt.2022.843816
Lei T, Li G, Luo C, Zhang L, Liu L, Stephen Gates R. An informative planning-based multi-layer robot navigation system as applied in a poultry barn. Intell Robot 2022;2(4):313-32. http://dx.doi.org/10.20517/ir.2022.18
Evaluating the Efficiency and Effectiveness of Unoccupied Aircraft Systems (UAS) for Monitoring Animals
The use of remote sensing to monitor animal populations has greatly expanded during the last decade. Unoccupied Aircraft Systems (UAS) allow vast areas to be surveyed efficiently regarding cost and time. However, when monitoring mobile animals, counting errors may occur. This project aims to evaluate the consequences of counting animals using UAS and recommend an efficient and effective UAS survey approach. Agent-based model simulations will be used with varying animal distributions and movement patterns to simulate animals on a digital landscape. Multiple competing UAS flight patterns will be investigated to determine the best approach for reducing erroneous counts. These complex simulations will be conducted within a high-performance computing environment using multiple animal movement-UAS survey configurations to produce thousands of simulated surveys and thus provide robust inference for UAS survey applications in remote animal detection.
The accompanying animation depicts a UAS surveying an area and is simulating a photo being taken at each yellow square. The green dot represents the starting point of an animal with its movements tracked by the orange line moving across the screen.