By Elton Ziki
AS we progress on the mechanisation path, it is pertinent to note that by the late 20th Century, electronically controlled hydraulics and power systems were the enabling technologies for improving machine performance and productivity.
With electronically addressable machine architecture, coupled with public access to global navigation satellite system (GNSS) technology in the mid-1990s, mechanisation, in the last 20 years, has been focused on leveraging information, automation and communication to advance on-going trends in the precision control of agricultural production systems.
Advances in machine system automation have increased productivity, convenience and reduced skilled labour requirements for complex tasks.
From mechanisation to
Today’s increasingly automated agricultural production systems rely on the collection, transfer and management of information by ICT to drive increased productivity.
What was once a highly mechanical system is becoming a dynamic cyber-physical system (CPS) that combines the cyber or digital domain with the physical domain.
Precision agriculture, or precision farming, is a systems approach for site-specific management of crop production systems.
The foundation of precision farming rests on geospatial data techniques for improving the management of inputs and documenting production outputs.
As the size of farm implements and machines increases, farmers are able to manage larger land areas.
A key technology enabler for precision farming resulted from the public availability of GNSS, a technology that emerged in the mid-1990s.
GNSS provided metre, and eventually decimetre, accuracy for mapping yields and moisture content.
A number of ICT approaches were enabled by precision agriculture which includes precision planters, sprayers, fertiliser applicators and tillage instruments.
Sources of data for site-specific maps can be satellite imaging, aerial remote sensing, GIS mapping, field mapping and derivatives of these technologies.
Some novel concepts being explored suggest that management strategies can be derived from a combination of geospatial terrain characteristics and sensed information. (Hendrickson, 2009)
A competitive technology for map-based precision farming is on-the-go sensing systems, based on the concept of machine-based sensing of agronomic properties such as plant health, soil properties and presence of disease or weeds.
These sensor capabilities essentially turn the agricultural vehicle into a mobile recording system of crop attributes measured across the field.
In fact, current production platforms are increasingly becoming tools for value-added applications through ICT.
Around the turn of the 21st Century, GNSS technology had become so precise and accurate that it had outpaced the requirement for the early phases of precision farming and become commercially viable for enabling a number of automatic-guidance applications. (Han et al., 2004)
Advances in GNSS technologies include decimetre to centimetre accuracy by using signals from a geospatially known reference point to correct satellite signals.
One premium example is a real-time kine-matic global positioning system (RTK-GPS) technology that reduces fatigue and lowers the skill level required to achieve high-performance accuracy in field operations.
In short, in less than 20 years, GPS technology went from being an emergent technology to a robust, mature technology that has optimal capabilities for production agriculture.
A number of solutions are emerging today for achieving high-precision accuracy through various reference-signal configurations, for example, RTK-GPS, multiple satellite systems, sensor fusion with complementary sensors and multiple sources of corrections.
On the next level of evolution, automatic guidance systems appeared that managed steering for an operator through automatic control.
Automatic guidance systems enabled precision operations depending on the type of GNSS signal and how it is integrated into the requirements of the agricultural operations.
GNSS technology enabled the management of inputs such as water, seed, pesticides and fertilisers with precision across the field.
For example, the chemical application to buffer zones and grassy waterways was reduced based on sensing of the field location of these features.
John Deere’s software product, SwathControl Pro, enabled farmers to manage the definition and execution of this capability.
GNSS technology provided the reference signal that enabled accurate vehicle location at the GNSS sensor, but precision control of the machine required several additions to the system that involved attitude correction, inertial sensors and implement control.
The ultimate in un-manned automation is the capability of driving complete field patterns under autonomous management of the tractor-implement functions without frequent operator intervention.
This allows a very rudimentary form of path planning, integrated with automatic guidance that can increase productivity by managing the paths a vehicle must follow.
Like precision agriculture, precision guidance creates data from its precision operations that could be used in crop management.
System automation and control
Until recently, automation has been focused on functions that depend on GNSS or direct sensing.
HarvestLab™, a sensing technology, uses near infrared (NIR) reflectance sensing to detect the moisture content of forage and adjust the cut-length of harvested material.
This control strategy can significantly reduce the energy consumption for harvesting forage with no degradation in the ensiling process.
The results are a significant reduction in fuel consumption in the harvest operation and a high-quality cut, which enables proper forage preservation.
In the last few years, the commercial application of telematics devices on machines has been increasing in agriculture, thus empowering a closer connection between farmers and dealers in managing machine uptime and maintenance services.
It is clearly within the vision of the agricultural industry to develop advanced capabilities that leverage ICT innovations.
As ICT continues to penetrate production systems, a massive network is being developed of machine systems that are platforms for value creation well beyond productivity from agricultural mechanisation intended for the farmer or the farm site.
These systems are collecting and managing information with potential value in downstream value-chain operations that use crop or drive systems to achieve environmental sustainability.
As intelligent mobile equipment for worksite solutions has evolved over the last 20 years, agricultural mechanisation has also evolved from a bottom-up integration of the foundations of ICT applied to basic mechanisation systems required for crop production.
Although these advanced technologies are not uniformly distributed among platforms and production systems, where they exist, there are opportunities to leverage ICT to increase production systems capabilities.
Looking ahead, it is expected that the business value of ICT will expand to additional platforms in agricultural productivity.
Agronomic data will create new opportunities for intensive modeling and simulation that can improve production efficiency by anticipating the impact of weather and various production methods.
In the future, ICT will enable the development of new platforms that can provide more support to production agriculture by taking advantage of opportunities to connect farmers, the value chain, and society in ways that are beyond present capabilities.
The German-funded iGreen project, for example, is working on location-based services and knowledge-sharing networks for combining distributed, heterogeneous public and private information sources as steps toward future ICT systems. (iGreen, 2011)
No doubt agricultural mechanisation will be a key factor to achieving our increased productivity goals and feeding a growing planet, Zimbabwe in particular.
Looking ahead, agricultural machines will become data-rich sensing and monitoring systems that can map the performance of both machines and the environment they work on with precision resolution and accuracy, and this capability will unlock levels of information about production agriculture that were heretofore unavailable.
Production and productivity in key locomotive or anchor sectors which are agriculture, mining, manufacturing and tourism will be critical in achieving the overall goal of sustainable economic growth.
This will catalyse economic growth in other sectors of the economy, including the Small and Medium Enterprises (SMEs) and the semi-formal activities that are ordinarily at the margins of the economy for inclusive growth in line with Sustainable Development Goals (SDG) 2 and 13 and Vision 2030 as well as the National Priorities Agriculture and Food Systems Transformation Strategy (AFSTS).