Volume 69, Issue 4 - Special Issue: Smart agriculture / Jürgen Beyerer, Georg Bretthauer, Thomas Längle

The demand for internet access and data transfer is the key driver of mobile communications technologies. The next step is wireless real-time networking. The “Tactile Internet” demands completely new technologies. Low latency, short response times, high reliability and resilience, a network of edge clouds and security are basic requirements. Therefore, 5G is developed. Novel use cases are industrial automation, autonomous vehicles, e-health, and the Internet of Things. For agriculture, 5G is a crucial technology enabler for robotization and Smart Farming processes. Agriculture needs own ad-hoc radio network solutions to efficiently provide coverage and manage data transfer, privacy and flexibility. The synergy of cellular and local mesh communications can solve the problem of unreliable communication and allows farmers to take full control of their data.

Plant production in indoor farming systems offers significant advantages compared to open field or greenhouse production systems. Especially in terms of quality and the ability for automation the system is superior to the conventional production systems. Concerning resource consumption indoor farming has considerable advantages in regard to water consumption and the use of pesticides. The main disadvantage is the high consumption of electrical energy. Taking advantage of the specific benefits or eliminating the disadvantages, for example by using renewable energies, different potentials and fields of application for indoor farming arise. The paper outlines the potentials and future fields of application of indoor farming considering the specific differences to conventional production systems related to resource consumption, quality and automation.

The selection of training data determines the quality of a chemometric calibration model. In order to cover the entire parameter space of known influencing parameters, an experimental design is usually created. Nevertheless, even with a carefully prepared Design of Experiment (DoE), redundant reference analyses are often performed during the analysis of agricultural products. Because the number of possible reference analyses is usually very limited, the presented active learning approaches are intended to provide a tool for better selection of training samples.

This article describes a modular and scalable charging and navigation concept for electrified field robots and other agricultural machines. The concept consists of an underbody charging system on a trailer and a modular navigation box. The underlying conductive charging process is compared to other charging techniques. Charging time in relation to charging current and mean power consumption in field use is displayed. In the navigation box, data of various sensors are combined by means of multi-sensor fusion regarding the precise time of arrival. Time synchronization is achieved by a novel method for compensating the data latency jitter by employing Kalman-based timestamp filtering. Furthermore, navigation functionalities, such as motion planning and mapping, are presented.

Today agriculture equipment is characterized by a high level of productivity and automation. Tractor-implement systems establish productivity by increasing working width and higher operational speed supported by larger storage volumes and increased tractor power availability. Dimensions and weight of tractor – implement systems have become the most important limitations for further growing productivity. A trend from Bigger, Faster, Wider towards Smart, Connected and Modular is under discussion. Modular and scalable systems with a high and variable degree of autonomy have potential to deliver configuration capability regarding actual task and field conditions while providing scalability of peak performance, productivity and economy. Possible machine concepts can consist of a tractor with automated and modular process units and / or autonomous self-propelled systems working as single unit or in a fleet (swarm). Swarm operation requires an adaptive and multimodal frontend to manage and lead a heterogeneous swarm of smart process applications. The example Feldschwarm, shown in the paper, is designed to the requirements of tillage and seeding in a first step.

This work describes an approach to calculate pedological parameter maps using hyperspectral remote sensing and soil sensors. These maps serve as information basis for automated and precise agricultural treatments by tractors and field robots. Soil samples are recorded by a handheld hyperspectral sensor and analyzed in the laboratory for pedological parameters. The transfer of the correlation between these two data sets to aerial hyperspectral images leads to 2D-parameter maps of the soil surface. Additionally, rod-like soil sensors provide local 3D-information of pedological parameters under the soil surface. The goal is to combine the area-covering 2D-parameter maps with the local 3D-information to extrapolate large-scale 3D-parameter maps using AI approaches.

Hyperspectral sensor systems play a key role in the automation of work processes in the farming industry. Non-invasive measurements of plants allow for an assessment of the vitality and health state and can also be used to classify weeds or infected parts of a plant. However, one major downside of hyperspectral cameras is that they are not very cost-effective. In this paper, we show, that for specific tasks, multispectral systems with only a fraction of the wavelength bands and costs of a hyperspectral system can lead to promising results for regression and classification tasks. We conclude that for the ongoing automation efforts in the context of cognitive agriculture reduced multispectral systems are a viable alternative.

Population rise, climate change, soil degradation, water scarcity, and food security require efficient and sustainable food production. Aquaponics is a highly efficient way of farming and is becoming increasingly popular. However, large scale aquaponics still lack stability, standardization and proof of economical profitability. The EU-INAPRO project helps to overcome these limitations by introducing digitization, enhanced technology, and developing standardized modular scalable solutions and demonstrating the viability of large aquaponics. INAPRO is based on an innovation a double water recirculation system (DRAPS), one for fish, and the other one for crops. In DRAPS, optimum conditions can be set up individually for fish and crops to increase productivity of both. Moreover, the integration of digital technologies and data management in the aquaculture production and processing systems will enable full traceability and transparency in the processes, increasing consumers’ trust in aquaculture products. In this paper, the innovations and the digitization approach will be introduced and explained and the key benefits of the system will be emphasized.