Location: National Peanut Research Laboratory
Project Number: 6044-30100-001-010-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Jun 26, 2026
End Date: Jun 25, 2029
Objective:
Aflatoxin contamination in peanut is a significant economic problem in the United States peanut industry. The costs of ensuring that only high-quality peanuts are allowed to enter the US and international markets directly and negatively affect the peanut industry. These costs are incurred at all segments of the peanut industry including farmers, buying points, shellers (processors), and final product manufacturers. Eliminating aflatoxin would have direct impact on improving the competitiveness of all industry segments and result in long-term growth in domestic and international markets positively impacting farmers, ranchers, producers, American agriculture, rural America, and US consumers. This collaborative project makes unique contributions to addressing the aflatoxin problem from the field to the shelling plant by taking three distinct approaches. The first approach uses a crop simulation model and publicly available sets to identify areas in peanut fields that are prone to aflatoxin contamination. The second approach explores spectral imaging technologies, including recent advances in using the terahertz (THz) range of the electromagnetic spectrum, for sensing and provides the opportunity for a quantum leap forward in Aspergillus flavus and aflatoxin detection on postharvest peanuts. The third approach monitors and predicts aflatoxin hotspots in peanut storage through targeted intervention and validation.
Approach:
This project makes unique contributions to addressing the aflatoxin problem from development of the AflaRisk decision support system (DSS) which will enable peanut shellers and peanut growers to assess the potential of site-specific aflatoxin contamination in peanut fields prior to harvest. The DSS will allow growers and shellers to take preventive action to limit cross contamination during harvest either by collecting field samples for aflatoxin, segregating areas with higher risk during harvest, or a combination of both. Use THz spectroscopy to identify the chemical signatures of A. flavus and aflatoxin contamination in the hull, seed coat, and kernel of individual peanuts. The implementation of this technology will be in shelling plants to identify A. flavus presence and aflatoxin contamination in whole in-shell peanuts and shelled peanut kernels. During storage, computational fluid dynamics (CFD) will be employed for hotspot identification to targeted intervention and validation. Concurrently, a prototype AI-driven warehouse management application trained on field and CFD datasets to predict hotspot risk and temperature/RH trajectories under different ventilation strategies, enabling practical decision support without reliance on specialized CFD modeling.
Specifically, the cooperators will work with ARS in the following approaches: 1) Developing improved prediction systems to identify high-risk fields to allow segregation of peanuts based on quality, 2) Develop methods to improve performance in peanut processing facilities based on incoming quality parameters (mainly aflatoxin), and 3) Conduct technology transfer activities with the US peanut industry to improve the quality of US peanuts.