Agriculture 4.0 is emerging and requires an expanded array of sensors. Agriculture 4.0 refers to systems that use drones, robotics, the Internet of Things (IoT), vertical farms, artificial intelligence (AI), renewable energy, and advanced sensor methodologies. Agriculture 4.0 is similar to Industry 4.0 in some ways: Where Industry 4.0 was designed to support automation and mass customization of production processes, Agriculture 4.0 is expected to support autonomous operations and mass customization of farming practices in micro-environments.
By integrating digital technologies into agriculture, agricultural operations can direct the resources needed to increase yields, reduce costs and minimize crop damage, water, fuel and fertilizer use. This FAQ examines the sensor technologies being developed for Agriculture 4.0, including plant wearables and hyperspectral imaging, the EU Interoperability Program and the Agricultural Analysis System, and the security challenges associated with wireless sensor networks and the Internet of Things things in Agriculture 4.0.
Worn plant materials
Graphene and optical fibers are two technologies used to develop wearable plant sensors. Graphene sensors can measure the time it takes for different crops to move water from the roots to the lower and upper leaves. Researchers are initially using these sensors to help develop plants that use water more efficiently. In the longer term, these graphene sensors on a ribbon (also called “plant tattoos”) are expected to support the design of low-cost, high-performance sensors for Agriculture 4.0 applications (Figure 1) and help improve the efficiency of irrigation systems. The process used to make the sensors can produce devices several millimeters in diameter with features as small as 5 μm. Small feature sizes increase sensor sensitivity. The conductivity of the graphene oxide in these sensors changes in the presence of water vapor, allowing the measurement of transpiration (the release of water vapor) from the leaf.
Figure 1: Graphene on tape can be used to fabricate wearable plant sensors. (Image: Iowa State University)
Fiber Bragg grating (FBG) sensor technology is also being developed for agricultural applications. The FBG acts as a slit filter that reflects a narrow portion of light centered around the Bragg wavelength (λb) when illuminated by a broad light spectrum. It is manufactured as a microstructure inscribed in the core of an optical fiber. Unlike graphene sensors, which are an emerging technology, FBG sensors are already used in several fields, including aerospace, civil engineering, and human health monitoring. FBG sensors can be manufactured with high sensitivity, small size and light weight. In the case of agricultural sensors, the inherent sensitivity to strain (ε) and temperature changes (ΔT) of FBG technology is combined with a moisture-activated polymer to detect changes in relative humidity (ΔRH) in the ambient air. In addition, FBG sensors can be multiplexed to support monitoring of both plant growth and environmental conditions in a single device. The FBG designed for agricultural applications consists of three segments, one for the ε sensor, one for ΔRH monitoring and a third optimized for ΔT measurements. It was fabricated using a commercial FBG with a grating length of 10 mm, λb of 1533 nm with a stretchable acrylate coating. The coating protects the FBG and improves its adhesion to the plant stem.
From multispectral to hyperspectral imaging
Multispectral imaging is an established agricultural sensing technology. It can detect subtle changes in plant health before visible symptoms are apparent. For example, a drop in the plant’s chlorophyll content can be detected before the leaves are visibly yellow. Multispectral sensors use wavelengths from 712 to 722 nm (the red band), where indications of stress are most easily identified. Multispectral imaging can be implemented using fixed installations where the sensors move back and forth on a rail system in a greenhouse or across an open field. They are also very suitable for carrying aloft with a drone. For example, in one configuration, a drone-based multispectral imaging system can scan a 100-acre field (400 feet above found with 70% overlap) in less than 30 minutes (Figure 2). Some of the advantages of multispectral imaging include:
- Early detection of the disease
- Improved irrigation and water management
- Faster and more accurate plant counts to optimize fertilizer application and pest control
- Cost reduction from the automation of activities previously performed by walking the fields
Figure 2: Drone-based multispectral cameras can take less than half an hour to scan a 100-acre field. (Image: Coptrz)
The main difference between today’s multispectral sensors and emerging hyperspectral sensors is the bandwidth (the number of bands and how narrow the bands are) used to represent the electromagnetic spectrum data. Multispectral imaging typically uses 3 to 10 bands to cover the relevant spectrum. Hyperspectral images consist of hundreds or thousands of narrower bands (10 to 20 nm), providing greater resolution and covering a wider spectrum. Spectral resolution, the ability to capture a large number of narrow spectral bands, is an important feature of hyperspectral imaging compared to multispectral imaging. Other advantages of hyperspectral imaging include:
- Higher spatial resolution and ability to distinguish smaller elements,
- Higher temporal resolution and the ability to more quickly sense important changes in the environment, such as the need for irrigation
- Higher radiometric sensitivity and ability to detect small differences in radiated energy
Hyperspectral imaging sensors provide a highly detailed electromagnetic spectrum of agricultural fields, making them a useful tool for detecting smaller and more localized variations in important soil characteristics and degradation, as well as changes in crop health and fitness. The increasing use of sensors in Agriculture 4.0 and the addition of higher resolution sensors such as hyperspectral imaging is driving the use of big data and raising concerns about data security, data integrity and privacy. Addressing these concerns is a major focus for the EU’s ATLAS programme.
Agricultural Interoperability and Analysis System
The EU-funded Agricultural Interoperability and Analysis System (ATLAS) project aims to develop an open platform to support innovation and Agriculture 4.0. ATLAS is one of the EU’s Horizon 2020 research and innovation programmes. The Fraunhofer Society manages the project. It addresses the current lack of data interoperability in agriculture by combining agricultural equipment with sensor systems and data analytics. The resulting platform is expected to support the integration of hardware and software interoperability from a wide variety of sensor systems and expand the benefits of digital agriculture. ATLAS aims to develop an open interoperability network for agricultural applications and build a sustainable ecosystem for innovative data-driven agriculture (Figure 3). ATLAS is based on field sensor networks and multi-sensor systems to provide the big data needed to realize Agriculture 4.0.
The ATLAS platform is expected to support the flexible combination of agricultural machinery, sensor systems and data analysis tools to overcome the current lack of interoperability and enable farmers to increase their productivity sustainably using the latest digital technologies and data. ATLAS will also define layers of hardware and software to enable the acquisition and sharing of data from multiple sensors and the analysis of that data using a variety of specialized analysis approaches. The program will demonstrate the benefits of Agriculture 4.0 through a series of pilot studies across the agricultural value chain and will conclude by defining the next generation of standards needed to continue the growing adoption of a data-driven architecture.
Wireless Sensor Networks and Security
Wireless Sensor Network (WSN) and Internet of Things (IoT) solutions are widely used in Agriculture 4.0, providing numerous benefits to farmers. However, the interconnection between various sensors and networked devices, which may contain unpatched or outdated firmware or software, creates opportunities for network insecurity and opens various attack vectors, including device attacks, data attacks, privacy attacks, network attacks, and so called
The increasing use of automation and even autonomous operations to improve yields also raises safety concerns. In addition to ATLAS, the European Union’s Horizon 2020 research and innovation programs focus on the development of network traffic monitoring and classification tools for use in Agriculture 4.0 systems. Effective traffic monitoring is expected to play an essential role in protecting assistants and users from the effects of network attacks. Network traffic analysis and classification tools are being developed for Agriculture 4.0 based on machine learning (ML) methodologies to help mitigate threats to WSNs and other IoT-related assets.
The implementation of Agriculture 4.0 relies on the increasing use of WSNs to improve yields and reduce costs for farmers. It also requires the development of new sensing modalities, such as plant-based wearables using graphene- and FBG-based sensors, and the expansion of existing sensing modalities, such as the shift from multispectral to hyperspectral imaging. The EU’s ATLAS program is designed to improve interoperability and make the most of the growing variety of sensor and data analysis technologies. Improvements in network security will also be essential to ensure the security, integrity and privacy of data in Agriculture 4.0.
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