Agriculture Smart Data Lab

Lab Overview

The advent of advanced instrumentation, detailed environmental data, and precision treatment capabilities in agriculture, provides new and compelling opportunities to apply Data Science to Agricultural Engineering. The Texas A&M Institute of Data Science (TAMIDS) collaborates with Texas A&M’s Department of Electrical & Computer Engineering, the School of Performance Visualization and Fine Arts, AgriLife Research, AgriLife Extension, and Texas A&M University Corpus Christi in a cross-disciplinary research program that integrates across foundations and practice by:

  • using machine learning techniques to model the influence of plant genetics, the environment, and treatment factors on plant growth;
  • developing algorithms that adaptively optimize treatment over the growing season to meet goals for yield and cost; and
  • embodying these algorithms in decision support systems that can deliver practical recommendations for crop treatment based on available knowledge.

Projects

Members

Collaborators

  • People: Pancho Abello, Mahendra Bhandari, Craig Bednarz, Salvatore Calabrese, Bardia Heidari Haratmeh, Timothy Goebel, Fouad Jaber, Juan Landivar, Robert Lascano, Pankaj Pal, David Parker, Payton Paxton, Nithya Rajan, Avay Risal
  • Organizations: Texas Water Resource Institute, Texas A&M Corpus Christi, Texas A&M AgriLife Research, Texas A&M AgriLife Extension, Texas A&M Departments of Agricultural Economics, Soil & Crop Science, Biological & Agricultural Engineering, and Animal Science, Goanna Ag, West Texas A&M University, Texas A&M Beef Center,

Publications

  • Yalong Pi, Egleu Mendes, Luis Tedeschi, Jian Tao, Siddhanth Reddy, Nick Duffield (2023), Using Computer Vision Pixel Segmentation to Detect Beef Cattle Feeding Behavior, submitted
  • Haoyu Niu and YangQuan Chen (2024). “Smart Big Data in Digital Agricultural Applications: Acquisition, Advanced Analytics, and Plant Physiology-informed Artificial Intelligence.” Springer Nature, 2024
  • Haoyu Niu, Nicholas Duffield, and Juan Landivar (2023). “Classification of cotton water stress using UAV-based RGB image and convolutional neural networks.”, submitted
  • Avay Risal, Haoyu Niu, Murilo M. Maeda, Mahendra Bhandari, Juan Landivar, Jose Landivar, Nick Duffield, Craig W. Bednarz, Paxton Payton, Pankaj Pal, Robert J. Lascano, Timothy Goebel (2023), Improving irrigation management of cotton with small unmanned aerial vehicles (UAV) in Texas high plains, submitted.
  • Haoyu Niu and YangQuan Chen (2022). Towards Tree-level Evapotranspiration Estimation with Small UAVs in Precision Agriculture.” Springer Nature, 2022

Funding

  • [USDA 2023] Harnessing Precision Livestock Farming to Support Smart Agriculture for Sustainable Beef Cattle Production, USDA-NIFA, PI L. Tedeschi, Co-PIs N. Duffield, Y. PI, J. Tao, 09/01/2023-08/31/2024, $103,563
  • [TWRI 2023a] Projection of Hydrologic Intensity Duration Frequency Parameters and Their Uncertainties Based on Climate Projections for 21st Century in the State of Texas, PI Bardia Heidari Haratmeh. Co-PIs, Fouad Jaber, N. Duffield, TWRI/TEES Exceptional Water Item, 09/01/2023-08/31/2025, $249,510
  • [TWRI 2023b] Advanced Irrigation Management System Guided by Remote Sensing and Artificial Intelligence, PI C. Bednarz, Co-PIs, M. Bhandari, N. Duffield, Juan Landivar, David Parker, TWRI/TEES Exceptional Water Item, 09/01/2023-08/31/2025, $ 350,000
  • [TWRI 2023c] Optimizing Agricultural Water Management: A Climate-Smart Approach to Reduce Carbon Intensity and Harness Carbon Market Opportunities, PI Nithya Rajan, Co-PIs Salvatore Calabrese, Nick Duffield, Pancho Abello, TWRI/TEES Exceptional Water Item, 09/01/2023-08/31/2025, $250,000
  • [TWRI 2021] Development of an Unmanned Aerial System (UAS)-based System to Improve Water Use Efficiency of Cotton by Monitoring Crop Water Use and Biomass Accumulation. PI M. Maeda, Co-PIs M. Bhandari, N. Duffield, C. Bednarz, J. Landivar, TWRI/TEES Exceptional Water Item, 09/01/2021-08/31/2023, $620,000

Events


Crop Growth Modeling & Smart Irrigation

Team: Mahendra Bhandari, Craig Bednarz, Nick Duffield, Timothy Goebel, Juan Landivar, Robert Lascano, Haoyu Niu, Pankaj Pal, David Parker, Payton Paxton, Janvita Reddy, Avay Risal, Jian Tao

Classification of Water Stress in Cotton via Convolutional Neural Networks

Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, promoting sustainable agricultural production. Crop evapotranspiration is a key quantity in enabling informed irrigation decisions. The emergence of Unmanned Aerial Vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. This project introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN).

This research was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”’, “percent deficit of full irrigation”, and “time delay of full irrigation”. The CNN model was able to classify the cotton water stress using UAV-based RGB images with a 91% overall best prediction accuracy.


Evapotranspiration Estimation in Cotton Crops

Limited water supplies, increasing pumping costs, variable rainfall patterns, and competition with municipalities are some of the challenges faced by agricultural communities across the state. Consequently, timely delivery and efficient use of irrigation water are critical to the sustainability and long-term stability of agricultural production in Texas. Presently, irrigation scheduling decisions based on reference evapotranspiration (ET) are limited due to the lack of reliable and readily available in-field weather data and updated crop coefficients. Additionally, information about in-field variability of crop water demand is seldom considered in the decision-making process. As a result, the efficiency of irrigation is often low, which leads to wasteful use of water in areas that are already plagued by scarce water resources.

In this project, we propose the development of an Unmanned Aerial System (UAS) based crop monitoring system that calculates crop water use for irrigation scheduling and increased water use efficiency. To do this, our system will take the advantage of big data analytics and Artificial Intelligence (AI) on the UAS-derived phenotypic data and infield weather data to calculate actual crop evapotranspiration, biomass accumulation, and determine the timing and quantity of irrigation water needed.


Yield Prediction for Cotton Crops

In this project, we modified the CNN as a regression model for cotton yield prediction. Adapting the CNN architecture for cotton yield prediction while maintaining the image as the input introduces an intriguing approach to leveraging spatial patterns within the visual data to forecast agricultural outcomes. By transforming the output layer into a single neuron regression model, the network can learn to interpret intricate visual cues within the images to predict the quantitative yield values accurately. This unique fusion of image-based inputs and regression modeling expands the capabilities of CNNs, enabling them to decipher nuanced features within the visual data directly related to cotton yield prediction. 


Predicting Hydrologic Intensity in Texas from Climate Projections

Team: Nick Duffield, Bardia Heidari Haratmeh, Fouad Jaber, Haoyu Niu

There is a growing scientific and public consensus about the impact of changing climatic patterns on the intensification of extreme rainfall events. Existing stormwater infrastructure systems fail to adequately respond to the changing extreme storm events since they are designed with older manuals based on historical rainfall records. Thus, there needs to be a re-examination of the frequency and intensity of rainfall projections. A comprehensive, reproducible, and consistent methodology to process the projections and develop updated Intensity-Duration-Frequency (IDF) curves is needed for infrastructure design and management.

This project is the first to propose such a comprehensive framework to apply multiple approaches to the precipitation projection for Texas. The study will apply an array of traditional statistical methods and machine learning (ML) models to update the IDF curves for Texas for the next 80 years. The method will be scalable and transferable to other locations, thus enabling the scientific community and engineers to better adapt to future rainfall patterns.


Optimizing Agricultural Water Management: A Climate-Smart Approach to Reduce Carbon Intensity

Team: Pancho Abello, Salvatore Calabrese, Nick Duffield, Nithya Rajan

According to the 2020 Texas State Water Plan, irrigation will remain the largest water consumer in Texas for the next 30 years. However, many agricultural regions in the state are facing severe water shortages for irrigation, creating a need for effective water conservation strategies. The challenge lies in motivating farmers to adopt irrigation scheduling practices and transition to more efficient methods, which can be expensive. What if we could incentivize farmers through financial compensation for implementing water conservation practices, using emerging carbon markets? Would these incentives encourage them to make the necessary changes that would benefit water conservation efforts in Texas? This project explores the relationship between optimal irrigation practices, carbon intensity, and financial benefits. Our goal is to establish Texas A&M as a leading institution in evaluating agricultural water management practices for carbon markets.