A recent article in the Wall Street Journal: Farmers Plow Through Netflix While Plowing Fields, documented how autonomous vehicle technology in the form of satellite-assisted guidance systems is allowing farmers to binge-watch their favorite shows and movies from the comfort of their tractor cabs. These video monitors are similar to those found in business class, equipped with a stereo sound system and an ergonomically designed massage chair rather than the systems most of us have to endure crammed into an airplane’s economy class. Streaming videos from Facebook or YouTube often includes watching planting progress in other regions or checking out the counter cyclical competition from South American farmers’ rival crops. Some state-of-the-art tractor cabs not only have climate control and leather upholstery but also built-in refrigerators, coffee makers and even microwaves. It will not be long before farmers will be able to utilize their time more productively during planting and harvesting by controlling and monitoring the performance of their autonomous field machinery from the comfort of their offices running cab-less tractors remotely.
The downward trend of commodity prices, the shortage of qualified rural labor, and the demands on their time will continue to relentlessly drive them to become more efficient. The availability of more powerful information and communication technology (ICT) and more reliable autonomous vehicle technology will allow the more tech-savvy farmers to spend more time multitasking the business aspects of their agribusiness operations, including pursuit of alternative income generating sources such as growing hemp or field peas.
While grain operations professionals will never have the luxury of binge-watching their favorite shows and movies on the job, advances in these technologies will make it possible for fewer employees to operate larger-scale grain handling and processing facilities more efficiently. GPS-enabled guidance systems will allow farmers to plant and harvest fields faster than ever before. Self-steering tractors, combines, grain carts and tractor-trailers will bring grain faster to a grain facility, potentially 24 hours a day during the harvest season. In response, grain receiving, sampling, grading, and subsequent transfer to wet holding, drying and storage will have to become faster and more autonomous in order to keep up with farmers and avoid losing their business to a competitor down the road who can.
The first article in this series (in the March 2019 issue) focused on ICT, and autonomous vehicles and transportation. This article discusses automatic grain receiving, sampling, grading, and smarter equipment maintenance. Future articles will focus on real-time monitoring of inventory and quality, and lights-out and hygienic operations.
Wireless sensor networks, equipment maintenance
We all have heard about the Internet of Things (IoT), which is the extension of internet connectivity into physical devices and everyday objects such as “smart home” appliances (lighting fixtures, thermostats, refrigerators), home security systems and cameras, and smart speakers like Alexa who resides in my house. These devices contain electronics, internet connectivity, and other hardware such as sensors, that allows them to communicate and interact with other devices over the internet. They can be remotely monitored and controlled with your own smart phone or by a service provider (home security firm) or equipment supplier. Take, for example, hazard monitoring systems that include a number of sensors installed on grain conveyors to monitor bearing temperatures, belt rub in a grain leg, vibration of a motor, slip of a shaft pulley, and motor overload situations. These sensors create a wireless network that communicates with a facility’s automatic monitoring and control system. However, realize the data the operations manager sees on monitors comes from a cloud server owned and operated by the service provider. It is critical for corporate and cooperative grain companies to have written agreements in place on data ownership and third-party utilization. In addition to providing real-time analytics of equipment performance, the data also update maintenance schedules for each motor and machine equipped with wireless-enabled devices and monitors.
The data allow approaches such as reliability-centered maintenance (RCM) to determine the maintenance requirements of each physical asset in its operating context. RCM is similar to proactive and predictive maintenance, but it uses a more systematic approach to evaluate a facility’s equipment and resources to best combine about 35% preventive and 55% predictive maintenance. It results in a high degree of facility reliability and cost-effectiveness, reducing reactive maintenance to about 10%.
RCM recognizes that all equipment in a facility is not of equal importance to either the process or facility safety. Think of the difference between the importance of a bucket elevator bearing temperature monitor to the grain transfer process and dust explosion prevention versus a sensor monitoring outside temperature that is important but not critical to the grain transfer process or safety. RCM recognizes that equipment will fail differently. It also considers that a facility does not have unlimited financial and personnel resources, and thus prioritizes and optimizes needs. It utilizes real-time analytics by hazard monitoring consumable equipment (belt rubs), equipment that is prone to random failure patterns, or equipment systems where failure may be induced by incorrect preventive maintenance. RCM is one example of how the Internet of Things has arrived in grain operations facilities and results in a smarter approach to maintenance.
Receiving, sampling and grading
Wireless networks of sensors and equipment are also critical for making grain receiving, sampling and grading more automated and autonomous. Operating remote truck probing stations from the grain grading room is not new to the grain handling industry. Semi-automatic truck probing and sample analysis systems have been extensively used to streamline logistics since the late 1990s at facilities that can receive hundreds of trucks a day. Making that operation autonomous means eliminating the human operator and replacing that function by installing cameras and digital image processing capability. These systems will receive an autonomous tractor-trailer from a farmer and take over control once registered as arriving on the property of the grain facility. From that moment the tractor-trailer is guided by the facility’s monitoring and control system to the probing station, then to the scale, and from there to the correct receiving pit where a robotic arm opens the trailer gates to dump the load. Once grain flow stops and overhead cameras confirm the trailer is empty, the gates are robotically closed.
The truck is guided to the exit of the property where control is turned back over to the autonomous system of the farmer, which brings the tractor-trailer back to the field or on-farm grain storage system for the next load. By the time the tractor-trailer leaves, the data record of the entire transaction and a video clip documenting in GIF format the unload process already has been transmitted to the customer.
Cameras and digital image processing are also critical components to assess grain quality and assign a grade that in the past has required visual inspection of the sample and individual kernels or seeds by a trained grain grader. A number of years ago, technical experts from the Federal Grain Inspection Service (FGIS) spent substantial effort, time and money attempting to develop fully automated and autonomous grain grading systems at export facilities in New Orleans. Corn and soybean grading proved relatively straight-forward while wheat grading was apparently never resolved to the satisfaction of traders and official inspectors. So far, autonomous high-capacity grain grading systems have not been implemented in the United States. However, at the grain quality laboratory of the Bolsa de Comercio in Rosario, Argentina, a human-assisted semi-autonomous high-capacity grain grading system has been implemented successfully that allows for analysis of several million samples a year.
Currently, grain handling companies are not deriving enough of the value contained in each load of grain delivered to their locations and neither do farmers selling their crops. Buying and selling continues primarily on the basis of quantity of grain meeting a few easily identified standard grade determining factors such as damage, broken grain and fine material, and foreign matter. Moisture content in the United States is not part of the grade but it is measured at each point of sale to assess whether to discount the price for exceeding market moisture content, and whether to charge extra for shrink and drying expenses. However, the intrinsic value contained in each kernel of corn and soybean is its end use value such as amino acid profile for optimizing animal diets, amount of extractable starch for conversion into ethanol, amount of oil and protein for making high protein soybean meal, and fatty acid profile for making heathier oils for human food products.
Bench-top equipment in the form of near-infrared (NIR) analyzers has been available for decades but primarily in analytical labs of grain processing facilities and feed mills. Accuracy, reliability and speed of sample analysis has continued to improve as measurement technology has advanced. Analysis takes about one minute per sample and is determined by the machine automatically evaluating seven smaller subsamples. Data are automatically uploaded to cloud-based data networks and can be available in real-time company-wide.
Substantial effort has been devoted in recent years to develop reliable, accurate and fast online NIR sensors. Protein NIR online sensors have been available on wheat combine harvesters for a few years and farmers have benefited from premiums by segregating high protein wheat in the field. Online NIR sensors are about to make a big leap forward by becoming inexpensive enough to install on corn and soybean harvesters as well as on grain conveying and processing equipment. The critical question is not whether but when corporate and cooperative grain handlers with multiple facilities will begin to extract value from the intrinsic end use value of the grain they handle, dry, store and ship.
What service could they provide to their customers — both farmers from whom they buy and buyers to whom they sell — to utilize this data to maximize feed and processing value of aggregated grain segregated by end use quality attributes? Grain companies are at the nexus point between grain production and grain utilization. They do not need third-party services to document the end-use value of the grain they handle. Instead, they should cease the opportunity to do so before farmers equip their combines with online NIR sensors and transmit end-use value data directly to the purveyors of blockchain technology and from there to end users.
Therefore, the challenge for the grain handling industry is whether they will embrace the business opportunity to be a facilitator and coordinator of end-use value for their farmer customers, or whether they will allow third-party service providers to profit instead.