Recipient Organization
PURDUE UNIVERSITY
(N/A)
WEST LAFAYETTE,IN 47907
Performing Department
Ag & Biological Engineering
Non Technical Summary
Spoilage of grain in on-farm storage is becoming a major problem on U.S. farms in the Corn Belt. This hurts farmers not just because of the discounts or dockage received when they deliver grain to the elevator, but more so when lives are lost in the process of trying to unload a grain bin that has caked up causing non-flowable grain resulting from spoilage. With the increase in grain bin sizes for on-farm storage, it is most likely that farmers will continue to encounter more grain management challenges and possibly fatalities, if they are not appraised with new knowledge and technologies on how to manage large grain stocks. Anecdotal evidence suggests that less than 30% of grain bins on U.S. farms have some form of stored grain monitoring technology. Additionally, with the increased bin sizes, from an average of 30,000 bushels in the 80s and 90s to over 100,000 bushels today, it appears that farmers today are still trying to use management principles that they successfully used to manage small grain stocks for managing large grain stocks. With the advancement in sensors, software and smart phone apps, a lot of data is logged from sensors and displayed on simple dashboards that provide little insight on the quality status of the grain or any predictive diagnosis should there be a problem with grain going out of condition. Additionally, the effect of irregular and extreme weather patterns on the stored grain ecosystem needs to be addressed through year long monitoring of stored grain over several cycles of storage. There is an opportunity to revisit developing new insights on the stored grain ecosystem using advanced analytical tools for effective total stored grain management.While current grain monitoring technology based on temperature and relative humidity sensors mounted on cables hung from the roof of the bin are good tools for monitoring grain quality over time, most of the systems out there lack adequate and easy to use analytics based on fundamental understanding of stored grain ecosystems for informed decision making. Also, the use of CO2 sensors, which has been demonstrated by Ileleji et al (2006) as a reliable and quick method for detecting spoilage in a grain bin is weakened by the lack of rigorous data analytics and interpretation. Other tools such as the automatic insect monitoring probe (InsectorTM) commercialized by OPI Systems/Integris based in Calgary, Canada have not been widely adopted. It is hypothesized that a major reason for the lack of adoption of these systems is the lack of data analytics with easy to use expert decision systems for actionable management decisions. The proposed project explores what and how data from various field devices in stored grain can be analyzed for informed decision-making.
Animal Health Component
70%
Research Effort Categories
Basic
20%
Applied
70%
Developmental
10%
Goals / Objectives
The overall goal of this Hatch project is to investigate new innovative analytical approaches for data from stored grain systems field sensors for effective stored grain monitoring and diagnosis. The following specific objectives will be pursued: (1) implement a long-term monitoring protocol for CO2, temperature and relative humidity in stored bulk grain through various seasonal cycles; (2) Improve on current CO2 monitoring technology to quantify and differentiate biological activity in a stored grain mass due to grain respiration, fungi, and/or insect pests activity; (3) develop a CO2 evolution and movement model for real time tracking of stored grain conditions; (4) develop an expert management system with real-time grain quality tracking for improved management of large grain stocks in bins, (5) test the use of acoustic devices to monitor insect activities in stored grains, and (6) incorporate knowledge from Objectives 1-5 in extension outreach efforts. We would utilize the Post-Harvest Education and Research Center (PHERC) located at Purdue University Agronomy Center for Research and Education (ACRE) for data collection, model verification and testing. Collaboration will be sought from local grain elevators, and sensor/software technology companies working on stored grain systems monitoring.
Project Methods
Objective 1. Implement a long-term monitoring protocol for CO2, temperature and relative humidity in stored bulk grain through various seasonal cycles:One of the goals of this objective is to clearly understand the stored grain ecosystem in terms of its physical and biological changes of its confined environment by triangulating and analyzing data from multiple field devices (sensors) that measure CO2, temperature and RH within the stored grain bulk, and use this information to precisely estimate the extent of a problem, should unusual physical changes or biological activity be discovered in the grain bulk. This will be conducted by monitoring a total of about 32 temperature sensors mounted on five temperature cables in replicate 500 bu corrugated steel bins with about 350 bu of shelled corn at PHERC. The bins are also equipped with a headspace temperature and RH sensor and a center cables with RH sensors to determine the equilibrium moisture content (EMC) of the stored grain. The sensor data will be logged hourly using OPI-Integris software program. A CO2 sensor will be installed in the headspace to monitor CO2 concentrations through several storage cycles. Data monitoring will be conducted for the same grain in multiple seasons from the fall through to the summer. Periodic (biweekly/monthly) grain sampling protocol would be implemented for collecting samples to determine moisture during storage and grain grade after each storage cycle. Dr. Ambrose will work with Dr. Ileleji and his team on a sampling protocol for this objective.Objective 2. CO2 monitoring technology to quantify and differentiate biological activity in a stored grain mass due to grain respiration, fungi, and/or insect pests activity: Upon completion of this objective, we would be able to determine from the CO2, temperature and RH data whether a given biological activity in the stored grain mass is normal or abnormally high due to fungi, insect pest or both. This information will be used to verify the model to be developed in Objective 3 and to develop the expert grain management system. Storage experiments will be performed to monitor biological activity based on CO2, temperature and RH from four scenarios in stored grain (corn) bulk stored in 55 gallons steel drums. The drums will be equipped with a perforated false floor through which a fan could be used to aerate the stored grain. Corn to be used will be freshly harvested/timely dried to 14-15% moisture content (MC), and subsequently disinfected by freezing at -20oC for 7 days or fumigating with phosphine to ensure that all biological activity from insect pest or fungi is eliminated. The drums will be filled with corn allowing a headspace volume of a ¼ of the total volume of the drum. A CO2 sensor mounted in the headspace above the grain mass will be used to monitor biological activities of these scenarios. In scenario 1 (control), bioactivity primarily caused by grain respiration will be monitored. In scenario 2, bioactivity primarily caused by fungi will be monitored using the same set-up as in scenario 1, but with high-moisture (18-25%) grain confined in a bioactivity capsule (CBC) placed at the center of the bulk. In scenario 3, bioactivity primarily caused by insect pests will be investigated using two pest species, the maize weevil, Sitophilus zeamais and red flour beetle, Tribolium castaneum. Dr. Ambrose will work with the team in grading grain.Objective 3. Develop a CO2 evolution and movement model due to grain respiration, fungi activity and insect pest activity:We will utilize an existing comprehensive stored grain ecosystem model (PHAST) developed at Purdue University that accurately predicts the heat and mass transfer in upright corrugated steel structures as a function of historic weather conditions (Montross, 1999). The model predicts temperature and moisture content changes in a grain bulk over time as a function of natural convection currents and diffusion effects. We will modify this model by coupling a sub-model that allows for the prediction of CO2 production due to biological activity from grain respiration, fungi, and insect activity previously mentioned, as well as losses from the stored grain system.Several mathematical models that predict the movement of CO2 as a fumigant gas in bulk grain have been solved (Jayas et al., 1988, Alagusundaram et al., 1996 and Smith and Jayas, 2001). These models predicted the diffusion of CO2 introduced into the grain bulk. They were based on the transient transport of miscible fluids in an anisotropic porous medium governed by the partial differential equation given by Bachmat and Bear (1964), Bear and Bachmat (1991) and Huyakorn et al., (1986). The modified model will also take into account the gas loss from the bin due to leakage from cracks and openings and exchange caused by ambient wind effects, and adsorption of CO2 by grain (Cofie-Agblor et al. 1993).Objective 4. Develop an expert management system with real-time grain quality tracking for improved management of large grain stocks in bins:An expert management system with real-time tracking of grain quality will be developed. The expert system will work on the principle that if we know the initial conditions of the grain when it was stored that are critical to determining its deterioration rate under given prevailing storage conditions determined by weather, then the quality changes of the grain over time could be modeled. The initial grain conditions that determine quality changes such as MC, grain temperature, percentage of fines, mold damage and insect pest infestation will be investigated via modeling. Additionally, we will explore the use of data from a fungal susceptibility measurement tool using the Solvita® kit developed at Purdue by Dr. Stroshine and his team (Moog, 2006).Objective 5. Develop acoustic assessment method for effectiveness of fumigation treatments and assessment of insect infestations in large grain stocks in bins:Dr. Baributsa would lead the effort in Objective 5 in collaboration with Dr. Ileleji and his team. An acoustic detection platform will be developed based on frequency ranges of common postharvest insect pests and background noise considerations. The acoustic system will be based on the AEC (Acoustic Emissions Consulting) connected to a recorder system. The AEC system will include a sensor-preamplifier module (model SP-1L, Acoustic Emission Consulting [AEC] Inc., Sacramento, CA, USA) connected to an amplifier (AED-2010L, AEC Inc. Sacramento, CA, USA) and recorder system. The setup for monitoring and recording insect signals will be similar to that described in Herrick and Mankin (2012). These recordings will be augmented with the OPI-Integris Insector pitfall trap installed in the bins at PHERC, which is similar to the non-pheromone insect pitfall traps (Storgard WB Probe II traps) to enable direct comparison of the detection ranges and counts of trap-captured insects. Movement and feeding sounds of insects will be distinguished from background noise. Regression analysis will be used to evaluate acoustic activity levels detected per fumigation treatment in relation to trap capture data.Objective 6. Incorporate knowledge from Objectives 1-5 in extension outreach efforts: The knowledge and tools developed in Objectives 1 to 5 will be presented in conferences and extension workshops as we progress along in our efforts. We envisage that we will present some aspect of this work at one or two conferences and three extension workshops yearly. Participation and presentation of this work at industry expositions such as the Grain Elevators and Processing Society (GEAPS), commodity classic and farm equipment shows will be pursued. Additionally, research manuscripts, extension factsheets and extension publications will be developed and distributed via Purdue's College of Agriculture extension website.