Location: Northern Great Plains Research Laboratory
Project Number: 3064-21660-004-030-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 1, 2022
End Date: Aug 31, 2027
Objective:
AM#1
(1) Application of remote sensing imagery for forage and field crop biomass prediction; (2) cereal and broad leaf crops disease assessment through foliar image processing and other field image processing tools; and (3) assessment of circular bioeconomy (CBE) in crop and rangeland applicable to North Dakota.
(4) Application of single-board computer (SBE) for machine vision applications and machine learning in agriculture; (5) Development of circular bioeconomy (CBE) and net-zero agriculture (NZA) analysis frameworks.
Approach:
AM#1
Objective 1: (1) collect historical remotely sensed imaging data from various sources of selected fields of known biomass data collection; (2) perform a literature search to understand the range of applications of remote sensing in agricultural and rangeland scenarios; (3) develop simple methods of assessment (e.g., statistical models) and determine their effectiveness; (4) develop and train ML models based on various influencing parameters (image, soil, and weather); and (5) develop web-based user-friendly tool using HTML and javascript for stakeholders.
Objective 2: (1) conduct a literature review of foliar diseases on cereal and broad leaf crops and understand the various management strategies; (2) collect field images at different scales (leaf, subplot, and whole); (3) develop methodologies of rapid disease assessment; (4) develop ML and deep learning (DL) models to assess the disease severity; (5) other field problem specific image processing tools such as weed identification, stand count, grazing evaluation, and other LTAR projects relevant image processing applications will be performed; and (5) develop web-based tool for visualization and deployment.
Objective 3: (1) perform a literature search to understand the various aspects of CBE applicable to crop and forage production; (2) develop CBE systems analysis (simple and elaborate) and apply to study cases with existing fields; (3) conduct scenario analysis to evaluate the benefits of CBE; and (4) develop tools to determine the benefits and visualize the results at a different level of CBE adoption.
Objective 4: (1) finalize SLR review manuscript on the SBE applications in agriculture; (2) collect procedures and methodologies of implementing machine vision and ML models on RP4; (3) develop simple programs implemented on RP4 to test the system; (4) train machine vision algorithms on RP4; and (5) collect field images, train and test ML models on RP4 for agricultural applications.
Objective 5: (1) draft review of CBE in cropland agriculture; (2) perform SLR on the status of NZA in North American agriculture; (3) formulate an initial framework to conduct “what-if” scenario analysis on CBE and NZA of ND crops; (4) develop tools to implement the initial framework.
