BACKGROUND

Field of the Art

This disclosure relates to systems and methods for improving agronomics production and yield. More specifically, the present invention relates to systems and methods for aggregated harvesting yield based on autonomous vehicle deployment.

Discussion of the State of the Art

According to the UN's Department of Economic and Social Affairs, the world's population is over 7.6 billion people. It is projected to be north of 9.8 billion people in 2050 while annual global crop production growth has been hovering at only 1.4% per year. It won't take long before demand for food out-strips our capacity to grow it.

In an effort to boost productive crop yield—especially in face of increasingly unpredictable weather patterns—farmers are turning to technology solutions, which are sometimes generally referred to as Precision Agriculture or Digital Agriculture. The leaders in this space have begun to utilize the data stream produced by sophisticated farm machinery. This data is used to identify areas of improvement and risk mitigation.

Today's data analytics systems are quite advanced and sophisticated. However, it is still rather difficult to connect data produced by farm machinery and in-field sensors to cloud analytics systems because most farms and farm machinery is located in rural areas with poor network connectivity. The network connectivity and availability problem is especially acute in less developed countries because of lack of investment by the telecoms in these sparsely populated regions. For these reasons connected computation, which is traditionally available to other applications, is not a viable option for agribusinesses that want to leverage sophisticated computing systems to unlock data driven insights.

Timely data transfer is a key enabler for the end-to-end processing of agricultural data—especially when timely data processing is necessary to implement the insights that are provided by analytics systems. However, farmers struggle to connect their machines to a network infrastructure because of the limitations outlined above. Because of the poor network availability, communities with poor network connectivity typically rely on USB sticks that plug into a vehicle's onboard monitor to store data. At the end of the day or week, the USB sticks are collected and brought back to the office to ingest the valuable data generated by the machines and its sensors.

This current solution, however, is unreliable at best, and at worst, leads to complete failure and data loss. For example, there is no end of stories about lost, erased, stolen and confused USB sticks, which not only reduces the amount of available data, but also potentially exposes the downstream analytics system to false or inapplicable data. Take for example a tractor in Brazil planting a field of soy beans in Mato Grosso State. The largest planters pulled by these high horsepower tractors have the ability to plant over 48 rows at a time. It is easy to imagine the complexity when dealing with 10, 50 or 100 USB sticks across multiple tractors, combines, sprayers and spreaders that have worked across hundreds of fields in a single area. One misplaced or confused USB drive could result in thousands of acres of lost data.

This problem is exacerbated because of the closed-end nature of modern agricultural systems. For example, modern planters are not only planting the seeds themselves at precisely controlled populations, they are applying fertilizers and crop protection products at the same time. All of this is being tightly controlled and monitored by on-board systems that are logging the location of each seed with RTK level GPS positioning. Months later this field is harvested with a combine equipped with yield monitor that can almost instantaneously calculate the productivity of the field. The killer app for these systems is the ability to close the loop with the ‘as-planted’ map from the planter. By doing this farmers can evaluate the efficacy of seed selection and crop protection package. Couple this with soil sample data and historical weather and the relevant decision makers can make very informed decisions about farming practices for the coming crop year. As such, lost data, which may be caused by lost or misplaced USB sticks or dropped or non-existent network connectivity, can negatively impact an entire growing season (and not just real-time decisions that may have to be made on-site).

The lost or dropped data problem is further exacerbated when it comes to making real-time decisions on a farm based on insights provided by data analytics system. For example, in one crop per year environments, which includes much of the US Midwest, farmers typically must make the most out of every crop year since they only get one chance each year to succeed with any given crop (as opposed to, for example, other areas where farmers are typically planting multiple crops a year providing multiple pathways to profit). Over their lifetimes, such farmers are the primary decision maker on about 40 crops, and, as a result, do not have yield and production data readily available, and, more importantly, cannot afford to guess because a bad decision can prove to be economically disastrous. These farmers are increasingly turning to real time data to inform their mid-year crop protection and fertility decisions. In field sensors that measure soil moisture, soil temperature, ambient temperatures, relative humidity and wind speeds among many other parameters can give farmers the information they need to increase the probability of having a successful crop.

However, reliably connecting these sensors to downstream analytics systems with reliable connection with sufficient bandwidth has proven to be very complex task. The traditional approach of using legacy cell based technology, or dedicated networks or satellite connectivity has proven to be too expensive and the bandwidth too limited to be a viable option. In addition, as the number of sensors and devices increase in the future, due to cost, technology demand and other factors, strain on various networking technologies will only increase. For these reasons, farmers are simply unable to leverage the advances in Precision Agriculture and to improve yield and/or production.

In addition, farmers are becoming increasingly reliant on computing recommendation systems to obtain the best yield possible from their land tracts. For example, farmers are often using computing systems to obtain recommendation regarding which seeds to plant, which farm management practices to apply, which crop protection plans to apply, which tillage program to apply, etc. Farming analytics software (which can make data driven recommendations about how to obtain optimal output from a field) needs accurate and high-quality current and historic yield information to help analyze farming practices and make recommendations. However, yield data that farmers are able to obtain is historically noisy and inaccurate, and can be unreliable for this purpose. Often, this is because different harvesters (which may be deployed, for example to cooperatively harvest a land tract or a given area) use different yield monitors, which are not usually calibrated to each other. As a result, when yield data is plotted on a land tract, it can appear “zebra-striped” and/or generally noisy. This type of data results typically can cause the farming analytics software to output unreliable recommendations. In addition, yield data can be further affected by variables related to climate, weather, topographical and geographical conditions and contexts. Currently, farm analytics software is hampered by poor and/or unreliable yield data, and, because of the unreliable yield data, is unable to provide useful or valuable recommendations.

SUMMARY

The illustrative embodiments provide a method, system, and computer program product for enabling data-centric computing platform comprising device, edge, and cloud computing infrastructure for reliable data transfer and data processing in an end-to-end digital agriculture system. Specifically, the present invention enables device and edge computing on certain collected data, which enables deep analytics in a cloud computing device. More specifically, the present invention standardizes how IoT data from field devices is ingested and normalized, provides a systematic approach to divide and distribute data-processing workload, automatically scales data-processing tasks to accommodate a wide spectrum of data complexity and volume, and simplifies how AI/ML inferencing functions are built and deployed to edge devices.

As such the present invention is a departure from currently available edge computing solutions that address one or two of these challenges described above. For instance, AWS GREENGRASS® service permits users to run microservices (in the form of Lambda functions) on embedded edge gateways. AZURE® IOT EDGE offers similar functionality except in the form of explicitly containerized apps. But these services fail to enable sophisticated data-processing capability over a network of large number of IoT and edge devices before sending the aggregated data to the cloud. Unlike these systems which simply cannot deal with the multi-dimensional complexity that is present in working with field data, the present invention takes into account sensor information on the in-field machinery itself, as well as inter-communication between machinery in field without human intervention—while, at the same time, collecting and storing robust data sets in agricultural areas of little or perhaps no network connectivity. In one embodiment of the invention, aerial based drones or similar technology may be used as a means to monitor in-field assets, as well as collect, process, and move data sets physically while in memory or in persistent storage from one location to another, the latter of which may possess network connectivity at limited or intermittent availability.

The present invention enables real-time decision making in-field environments, especially ones that are bereft of network connectivity. For example, the data transport systems and methods disclosed herein enables coordination of a grain cart with a grain elevator that may otherwise not be connected to each other. More specifically, the present invention enables a grain cart to ramp up, ramp down, or otherwise continue operation based on space and availability constraints at a grain elevator. In another example, the drone may obtain pricing information from two or more grain elevators and provide to an in-field environment with little to no network connectivity. Armed with this data, the in-field operator may be enabled to make a decision about whether to drive extra miles for higher prices by computing, for example, the additional fuel cost associated with a further drive and whether it would yield a higher return in terms of higher prices being offered.

In addition, the present invention enables two-way communication between a data delivery vehicle and an edge compute node and/or cloud compute node. As such, when a data delivery vehicle travels to the edge of a cell, it can not only upload the data that it has received form a physical asset, but it can download other relevant information for in-field operations. For example, continuing the example above, it can download the latest grain elevator prices and/or offerings for various grain elevators in an area (which may not have connectivity either), and upload that data to an edge compute node that can make a variety of computations about which grain elevator a transport vehicle should travel to for maximizing returns, etc. The same systems and methods enable intelligent in-field and real-time decision making in other use cases or contexts as well. For example, the two-way data processing and data transport systems and methods described herein enable real-time in field decision making for, hauling scrap out of a construction site, for picking up asphalt for a construction site, etc.

Although various embodiments of the invention described herein are related to smart agriculture systems, the present invention may be used in any use case or scenario where IoT devices are deployed, including, but not limited to commercial and home automation, smart factories or smart manufacturing, smart cities, smart environment, smart agriculture, smart health systems, smart construction and heavy machinery environments. For example, IoT networks may be used for smart agriculture systems, water distribution systems, electric power distribution systems, pipeline control systems, plant control systems, light switches, thermostats, locks, cameras, alarms, motion sensors, construction device, and the like including any industrial environment.

Embodiments herein recognize that determining accurate agricultural or harvesting yields can be challenging in view of a myriad of factors, including variability in performance and efficiencies among harvester machines and machine types, lack of, or limited, historical yield data correlated with crop varieties, prevailing growth as well as harvesting climate, weather and topographical conditions, and limitations in communication infrastructure and technology which can adversely and directly affect accurate determination of harvesting yields, especially in real-time during actual harvesting operations.

In particular, provided is a method for normalizing harvest yield data from an operation for harvesting crops. The computer-implemented method comprises receiving combine harvest data from a harvesting vehicle, the combine harvest data obtained from a yield monitor sensor that is on-board the harvesting vehicle, the combine harvest data comprising data collected from a first portion of a land tract; receiving high-resolution harvest data from an data delivery vehicle, the high-resolution harvest data obtained from a sensor suite that is on-board the data delivery vehicle, the high-resolution harvest data comprising data collected from at least the first portion of the land tract; obtaining edge compute data, the edge compute data comprising additional information related to at least one of the land tract, the crop, historic weather information associated with an agricultural cycle, and farm practices data; segmenting the received combine harvest data, the received high-resolution harvest data, and the obtained edge compute data; applying a normalization model to the segmented data for normalizing harvest yield data, the normalization model applied to the received combine harvest data, the received high-resolution harvest data, and the obtained edge compute data; and computing a normalized harvest yield for at least a portion of the land tract based on the applied data normalization model.

Also provided is a data delivery vehicle including a processor and a non-transitory computer readable memory. The memory stores instructions, which, when executed in the processor cause operations comprising receiving combine harvest data from a harvesting vehicle, the combine harvest data obtained from a yield monitor sensor that is on-board the harvesting vehicle, the combine harvest data comprising data collected from a first portion of a land tract; receiving high-resolution harvest data from an aerial data delivery vehicle, the high-resolution harvest data obtained from a sensor suite that is on-board the aerial vehicle, the high-resolution harvest data comprising data collected from at least the first portion of the land tract; obtaining edge compute data, the edge compute data comprising additional information related to at least one of the land tract, the crop, historic weather information associated with an agricultural cycle, and farm practices data; segmenting the received combine harvest data, the received high-resolution harvest data, and the obtained edge compute data; applying a normalization model to the segmented data for normalizing harvest yield data, the normalization model applied to the received combine harvest data, the received high-resolution harvest data, and the obtained edge compute data; and computing a normalized harvest yield for at least a portion of the land tract based on the applied data normalization model.

Further provided is a non-transitory computer readable memory storing instructions. The instructions are executable in a processor device to cause operations comprising receiving combine harvest data from a harvesting vehicle, the combine harvest data obtained from a yield monitor sensor that is on-board the harvesting vehicle, the combine harvest data comprising data collected from a first portion of a land tract; receiving high-resolution harvest data from an aerial data delivery vehicle, the high-resolution harvest data obtained from a sensor suite that is on-board the aerial vehicle, the high-resolution harvest data comprising data collected from at least the first portion of the land tract; obtaining edge compute data, the edge compute data comprising additional information related to at least one of the land tract, the crop, historic weather information associated with an agricultural cycle, and farm practices data; segmenting the received combine harvest data, the received high-resolution harvest data, and the obtained edge compute data; applying a normalization model to the segmented data for normalizing harvest yield data, the normalization model applied to the received combine harvest data, the received high-resolution harvest data, and the obtained edge compute data; and computing a normalized harvest yield for at least a portion of the land tract based on the applied data normalization model.

