TECHNOLOGY ADVANCEMENTS


By Diganta Adhikari, PhD Andres Ferreyra, PhD John Fox Srikanth Ganesan Divya Sundaram


T


he Internet of Things, now generally known as IoT, promises to revolutionize agricultural decision-making. It’s exciting to think of deploying a vast network of sensors that can observe the on-farm


environment in real time and help deliver best agronomic practices that can boost a grower’s bottom line, increase production sustainability and ensure freedom to operate. The reality of implementation can be challenging, however. Some issues may be expected, such as connectivity difficulties, but there are other hidden and progressively bigger obstacles lurking along the way. These challenges can be divided into different categories, each of which can grow to monster-sized proportions.


Managing interoperability


Different pieces of hardware and software have difficulty “talking” with each other, as manufacturers of the different components use different data formats, communications protocols, authentication schemes and so forth. This introduces friction and makes it hard to scale systems. For example, different loggers use different data formats, the cloud services


We recommend implementing industry standard data exchange formats and using standard code lists whenever possible, as well as understanding the interoperability pros and cons of any IoT frameworks you consider adopting.


Next challenge: Security


There is another major challenge beyond interoperability: security. The ag technology security landscape is far from ideal. At the device level, basic firmware, authentication/authorization and encryption are difficult to maintain, especially when corporate structure and resourcing are still catching up to IoT-specific requirements. When wireless technologies are used, we can expect greater risk of malicious attacks to company or grower assets, but field instruments typically transmit on limited- bandwidth connections, which makes the over- the-air firmware updates (that keep devices secure) expensive and difficult.


Then, once the data are collected, can we trust it’s really coming from our devices? Malicious software and infrastructure attacks are becoming more frequent as well as more ingenious. Many recent


Malicious software and infrastructure attacks are becoming more frequent as well as more ingenious.


that provide data from them use different names or codes for the same variables, and units of measure are rarely provided along with the data. This often all adds up to the data’s meaning getting lost in translation.


Working within established technology frameworks (e.g., AWS, IBM, Microsoft, Google, etc.) is helpful because the different parts or steps the data go through are laid out consistently within each framework, but system observability (i.e., understanding what’s going on in each part of the system) may be difficult or require proprietary tools. Some necessary features may not even be available, such as edge processing, where we perform calculations on the IoT device itself to reduce communications bandwidth or react to local situations. And operating costs may be difficult to estimate when starting out. Moreover, communicating data between providers’ proprietary clouds may be difficult.


attacks on corporate infrastructure have exploited vulnerabilities in devices connected to the corporate network, such as digital cameras, DVRs, medical devices, etc. Devices considered for IoT should be accessible (remotely) for firmware, certificate and password updates. It’s also important to follow protocols for strong password, secure network and ecosystem interfaces.


Data quality


Being able to securely move data from a device into the cloud while preserving its meaning and being confident of its origin isn’t enough. There is always the possibility of having “bad data” due to a breakdown in quality somewhere in the system. Old, damaged, dirty sensors; communications errors due to faulty antennae or overgrown line-of-sight transmission paths; and improperly identified sensors due to outdated configuration data on the logger or in the cloud are only some of the possible sources of data quality issues.


Summer 2021 | Irrigation TODAY 19


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