Research Project: Advancing Technologies for Grain Trait Measurement and Storage Preservation

Location: Stored Product Insect and Engineering Research

2024 Annual Report

Objectives
OBJECTIVE 1: Improve stored grain management, technology and processing practices to maintain grain end-use quality by controlling or eliminating adverse storage environments, insect infestations. Sub-objective 1A: Develop an insect monitoring and identification device for behavioral study and pest management in food facility environments. Sub-objective 1B: Increase efficacy of fumigation of milled and whole grain products through improved monitoring and modeling of fumigant applications. Sub-objective 1C: Increase efficacy of insecticidal aerosol applications in grain processing facilities based on measurement and modeling of droplet distribution and deposition. OBJECTIVE 2: Resolve existing issues and develop new technologies and techniques to rapidly and accurately evaluate intrinsic grain and seed quality to improve breeding efficiency, marketability, end-product use and environmental influences. Sub-objective 2A: Develop imaging methods for the detection of hard vitreous amber color (HVAC) of Durum wheat seeds as a replacement for manual wheat inspection. Sub-objective 2B: Selecting maize seeds for breeding programs using single seed near infrared spectroscopy (NIR) to improve hybrid development.

Approach
United States farmers annually (2016-2018) grow 562 million metric tons of corn, soybeans, wheat, sorghum and other grains to supply the nation and the world with food, animal feed and biofuels. Our project goal is to improve U.S. grain quality and international competitiveness through the application of engineering principles to rapidly measure grain traits and to maintain grain and grain-based product quality after harvest. We propose to develop unique instrumented systems to rapidly measure quality or compositional traits for breeders when selecting traits for varietal development. We also propose to develop technology to detect and control insects and maintain product quality during handling, processing and storage. This research will lead to expedited development of varieties and hybrids by breeders; better systems and information for storage management by farmers and processors, resulting in better profitability and production efficiency, less waste and increased food availability using fewer resources.

Progress Report
ARS researchers in Manhattan, Kansas, continued work on improvement to stored grain management technology by focusing on processing practices to maintain grain end-use quality and by controlling or eliminating adverse storage environments and insect infestations under Objective 1. Under Sub-objective 1A, Artificial Intelligence (AI) state-of-the-art computer vision models, trained and evaluated classification models for insect identification. The model delivered impressive performance with high accuracy. Over 76% of insects were accurately assigned to the correct species with fast detection times of, at most, 36 milliseconds. The best-performing model was integrated and deployed on a smartphone and desktop computer and real-time detection of insects appeared as a continuous video stream. Fast detection and classification rates showed good potential for the model to be deployed on less expensive computer hardware, which is important for commercial applications. Overall, the study provided a framework for automatic and real-time insect detection and identification in a stored product environment that seems well suited for adaptability to a variety of hardware platforms. Fumigation efficacy in grain products through improved monitoring and modelling of fumigant applications, respectively, is addressed under Sub-objective 1B. Firstly, phosphine gas fumigation distribution inside moving railcars was quantified. Railcars are an often-overlooked study area but provide broad mobility for insect pests to travel and contaminate facilities. Secondly, fumigant distribution in grain structures was studied using Computational Fluid Dynamic (CFD) models with predictions compared with experimental measurements collected by sensors. Phosphine concentrations were also measured over time across the bulk grain volume contained in corrugated steel bins that had been sealed following published recommendations wherein phosphine fumigation was applied using three different methods of application. Results were plotted on contour maps to indicate areas of adequate concentrations to achieve insect mortalities as well as identify areas that did not receive adequate concentrations. Factors were identified that could improve the distribution of gas throughout, including better sealing and using air circulation. Alternatives to gaseous fumigation using aerosols were studied under Sub-Objective 1C. Aerosol insecticide applications with a high-pressure sprayer and a handheld fogging system were conducted in an experimental flour mill. Droplet counting instruments were used to plot insecticide distribution patterns in contour plots to show the extent and potential efficacy of treatment of the mill floor area. Related to this Sub-objective, the use of chlorine dioxide as a fumigant was tested on wheat and effects on flour quality measured. It was shown to be effective against lesser grain borer and it did not impact milling or end use quality of the wheat, indicating its suitability as a fumigant for wheat. Accurate inventory of the total amount grain contained in bulk storage structures is vital not only for producers and merchandisers, but also for U.S. agriculture for projecting exports and assessing food security. The Discrete Element Modeling (DEM) compressibility models developed by researchers in Manhattan, Kansas, allow for better estimation of the mass of grain in storage that accounts for the packing of the grain when loading storage and as it settles over time (pack factor). Models that accounted for time in storage and intrinsic properties of the grain, such as weight, were developed for nine cereal grain crops . Weight of the bulk grain is difficult to measure during storage, but this difficulty can be overcome by use of appropriate DEM models as shown by this research. Under Objective 2, work continued on developing new technologies and techniques that rapidly and accurately evaluate intrinsic grain and seed traits to improve breeding efficiency which has direct links to marketability, end-product use, and environmental influences. Development of imaging methods for the detection of hard vitreous amber color of durum wheat seeds, Sub-objective 2A, was completed by our research engineers. Back-lit transmission images revealed the internal components of a durum wheat seed and allowed us to measure the hard and soft endosperm and germ. The ratio of hard to soft endosperm is being investigated as an indicator of seed hardness in wheat, sorghum, and other grain crops. Work continued on development of more rapid evaluation of corn hybrids under Sub-objective 2B. Application of the near infrared spectroscopy instrumentation designed and developed to select maize haploid seeds was used in screening more breeding lines developed by collaborating researchers in Iowa and Florida from the 2023 field season. Application of the instrument has been extended to measuring protein in soybean exposed to heat and drought stresses and in flax.

Accomplishments
1. Automated, real-time video identification of stored product insects. Monitoring stored product insects is key to early pest detection and subsequent mitigation before large scale product losses can occur; however, this process can be time-consuming and require expertise for species identification. Automated real-time detection of pest activity is the solution to this problem. ARS scientists in Manhattan, Kansas, developed a detection unit in the form of a cylindrical probe containing a video camera system that was deployed with a deep learning model to identify the insects in the video feed. The model was trained to recognize five common stored grain insect species: the lesser grain borer, rusty grain beetle, red flour beetle, rice weevil, and saw-toothed grain beetle. The system achieved high classification accuracy with over 76% of insects immediately identified by the algorithm in real-time with near instantaneous detection time. The deep learning model was also deployed on a smartphone and similar accuracy was achieved. The study provided a framework for automatic and real-time insect detection and identification in a stored product environment that seems well suited for adaptability to a variety of hardware platforms.

2. Using chlorine dioxide as an alternative fumigant to methyl bromide. There is an increasing need for alternative fumigants to replace methyl bromide and provide options for controlling stored-product insect pests, especially those that have become resistant to the common fumigant phosphine. Chlorine dioxide gas can easily penetrate cells from living organisms and cause irreversible damage, suggesting it can be an effective alternative fumigant. ARS researchers in Manhattan, Kansas, tested the efficacy of chlorine dioxide as a fumigant for control of lesser grain borers, a common stored product insect found worldwide. Results showed that complete adult mortality and minimal egg survival were achieved when infested hard winter wheat was exposed to 500 ppm chlorine dioxide gas. In addition, exposure of wheat kernels to chlorine dioxide did not negatively impact the quality of flour, indicating that fumigation with chlorine dioxide gas would not negatively impact milling products from wheat. Overall, this finding indicates that chlorine dioxide could be an effective, alternative fumigant for some species of stored product insects and that its use to treat wheat should not negatively impact milling quality and end uses.

Review Publications
Badgujar, C., Armstrong, P.R., Gerken, A.R., Pordesimo, L.O., Campbell, J.F. 2023. Real-time stored product insect detection and identification using deep learning: System integration and extensibility to mobile platforms. Journal of Stored Products Research. 104. Article 102196. https://doi.org/10.1016/j.jspr.2023.102196.
Scheff, D.S., Arthur, F., Domingue, M., Myers, S. 2024. Combination insecticide treatments with methoprene and pyrethrin for control of khapra beetle larvae on different commodities. Insects. 15(1). Article 77. https://doi.org/10.3390/insects15010077.
Brabec, D.L., Lanka, S., Campbell, J.F., Arthur, F., Scheff, D.S., Zhu, K. 2023. Aerosolize insecticide spray distributions and relationships to storage insect efficacies. Insects. 14(12). Article 914. https://doi.org/10.3390/insects14120914.
Brabec, D.L., Grothe, S.M., Perez-Fajardo, M.A., Pordesimo, L.O., Yeater, K.M. 2024. Potential of flatbed scanner for evaluation of flour samples for dark specks and flour color. Cereal Chemistry. 101:508–517. https://doi.org/10.1002/cche.10758.
Norton, A.E., Brabec, D.L., Tilley, M., Yeater, K.M., Scheff, D.S. 2022. Quantification of methoprene aerosol deposition using reversed-phase high-performance liquid chromatography. Journal of Stored Products Research. 99: Article 102039. https://doi.org/10.1016/j.jspr.2022.102039.
Buenavista, R.M., E, X., Subramanyam, B., Rivera, J.L., Casada, M.E., Siliveru, K. 2023. Evaluation of wheat kernel and flour quality as influenced by chlorine dioxide gas treatment. Journal of Stored Products Research. 102. Article 102127. https://doi.org/10.1016/j.jspr.2023.102127.
Badgujar, C., Armstrong, P.R., Gerken, A.R., Pordesimo, L.O., Campbell, J.F. 2023. Identifying common stored product insects using automated deep learning methods. Journal of Stored Products Research. 103. Article 102166. https://doi.org/10.1016/j.jspr.2023.102166.
Pulivarthi, M.K., Buenavista, R.M., Bangar, S.P., Pordesimo, L.O., Bean, S.R., Silveru, K. 2023. Dry fractionation process operations in the production of protein concentrates: A review. Trends in Food Science and Technology. 22(6):4670-4697. https://doi.org/10.1111/1541-4337.13237.
Serson, W., Gishini, M., Stupar, R., Stec, A., Armstrong, P.R., Hildebrand, D. 2024. Identification and candidate gene evaluation of a large fast neutron-induced deletion associated with a high-oil phenotype in soybean seeds. Journal of Theoretical and Applied Genetics. 15(7). Article 892. https://doi.org/10.3390/genes15070892.
Gokhan, H., Armstrong, P.R., Mendoza, T.P. 2024. Rapid single flax (Linum usitatissimum) seed phenotyping of oil and other quality traits using single kernel near infrared spectroscopy. Journal of the American Oil Chemists' Society. https://doi.org/10.1002/aocs.12875.
Turner, A.P., Montross, M.D., McNeill, S.G., Casada, M.E., Petingco, M.C., Maghirang, R.G., Thompson, S.A. 2023. Bulk compressibility behavior for select crops. Applied Engineering in Agriculture. 39(5):509-518. https://doi.org/10.13031/aea.15593.
Brabec, D.L., Grothe, S.M., Athanassiou, C. 2023. Testing barley samples for potential insect infestations with a conductance mill. American Society of Agricultural and Biological Engineers. 39(5):535-541. https://doi.org/10.13031/aea.15663.