500 Naturwissenschaften
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This paper investigates four different mobile robots with respect to their drivingcharacteristics and soil preservation properties in an agricultural environment.Thereby, robots of classical design from agriculture as well as systems from spacerobotics with advanced locomotion concepts are considered to determine theindividual advantages of each rover concept with respect to the application domain.Locomotion experiments were conducted to analyze the general driving behavior,tensile force, and obstacle‐surmounting capability and ground interaction of eachrobot. Various soil conditions typical for the area of application are taken intoaccount, which are varied in terms of moisture and density. The presented workcovers the specification of the conducted experiments, documentation of theimplementation as well as analysis and evaluation of the collected data. In theevaluation, particular attention is paid to the change in driving characteristics underdifferent soil conditions, as well as to the soil stress caused by driving, since soilquality is of critical importance for agricultural applications. The analysis shows thatthe advanced locomotion concepts, as used in space robotics, also have positiveimplications for certain requirements in agricultural applications, such as maneuver-ability in wet conditions and soil conservation. The results show potential for designinnovations in agricultural robotics that can be used, to open up new fields ofapplication for instance in the context of precision farming.
Computer-image processing becomes more and more important in the analysis of data in biological and agricultural research and practice. However, robust image processing is highly de pendent on the histogram analysis algorithms used and the quality of the data being processed. The algorithm presented here aims to improve the accuracy of the classification of image data generated under complex boundary situations and inconsistent lighting conditions. Using the example of the determination of nitrogen content of tomato leaves and the qualitative determination of starch con tent of apples on the basis of color image processing, we showed that the developed algorithm is able to perform a robust classification and represents an improvement to simple histogram analysis.
Within the consortium “Experimentation Field Agro-Nordwest”, a practical concept for knowledge and technology transfer of digital competence in agriculture was created. For this purpose, the web-based e-learning system “SensX” was set up, consisting of videos, presentations and instructions. In addition, the classical e-learning concept was extended by data sets, student experiments and sensor data of plants acquired by a remote phenotyping robot. This resulted in a massive open online course (MOOC), which was tested with agricultural and biotechnology students in higher education at the University of Applied Sciences Osnabrück over two years. The evaluation process of “SensX” included an empirical survey, qualitative interviews of the participating students by an external institution and an evaluation of the concept by the lecturers.
Iron deficiency is a global issue and can lead to a variety of clinical pictures. The biofor-tification of vegetables with iron could complement the existing portfolio of iron-rich products, thus improving iron supply in the long term. In order to determine whether the iron-biofortified vegetables could meet this demand and would address appropriate target groups, a quantitative online survey was conducted in Germany. Based on 1000 consumer responses, a cluster analysis was performed. The results showed a four-cluster solution. The first cluster was holistically engaged, the second was fitness-affine but health unconcerned, the third cluster consists frugal eaters with a focus on medical prevention, and the fourth cluster are hedonists. No cluster focused its consumption on iron-enriched products, but instead all developed an individual mix of the three product groups.
Hyperhydricity (HH) is one of the most important physiological disorders that negatively affects various plant tissue culture techniques. The objective of this study was to characterize optical features to allow an automated detection of HH. For this purpose, HH was induced in two plant species, apple and Arabidopsis thaliana, and the severity was quantified based on visual scoring and determination of apoplastic liquid volume. The comparison between the HH score and the apoplastic liquid volume revealed a significant correlation, but different response dynamics. Corresponding leaf reflectance spectra were collected and different approaches of spectral analyses were evaluated for their ability to identify HH-specific wavelengths. Statistical analysis of raw spectra showed significantly lower reflection of hyperhydric leaves in the VIS, NIR and SWIR region. Application of the continuum removal hull method to raw spectra identified HH-specific absorption features over time and major absorption peaks at 980 nm, 1150 nm, 1400 nm, 1520 nm, 1780 nm and 1930 nm for the various conducted experiments. Machine learning (ML) model spot checking specified the support vector machine to be most suited for classification of hyperhydric explants, with a test accuracy of 85% outperforming traditional classification via vegetation index with 63% test accuracy and the other ML models tested. Investigations on the predictor importance revealed 1950 nm, 1445 nm in SWIR region and 415 nm in the VIS region to be most important for classification. The validity of the developed spectral classifier was tested on an available hyperspectral image acquisition in the SWIR-region.
Recording of Low-Oxygen Stress Response Using Chlorophyll Fluorescence Kinetics in Apple Fruit
(2023)
Long-term storage of apples (Malus x domestica, Borkh.) is increasingly taking place under Dynamic Controlled Atmosphere (DCA). The oxygen level is lowered to ≤ 1 kPa O2 and the apples are stored just above the Lower Oxygen Limit (LOL). Low oxygen stress during controlled atmosphere storage can lead to fermentation in apples if oxygen levels are too low. Chlorophyll fluorescence can be used to detect low-oxygen stress at an early stage during storage. The currently available non-imaging fluorescence systems often use the minimal fluorescence (Fo) parameter. In contrast, the use of chlorophyll fluorescence kinetics is insufficiently described. Therefore, this study aimed to gain more knowledge about the response of chlorophyll fluorescence kinetics to low oxygen stress in apples using a fluorescence imaging system. The results show that the kinetic fluorescence curves differ under aerobic and fermentation conditions. The fermentative conditions initiated a decrease in fluorescence intensity upon application of the saturation pulses during exposure to actinic light. This result was made at 18 °C and 2 °C ambient temperatures. Interestingly, the kinetic curve changed at 2 °C before fermentation products accumulated in the apples. Non-photochemical quenching (NPQ) decreased under fermentation conditions in the dark phase after relaxation. Upon entering the dark relaxation phase after Kautsky induction, ɸPSII began to increase. Under atmospheric oxygen conditions, ɸPSII reached values of 0.81 to 0.76, while under fermentation, ɸPSII values ranged from 0.57 to 0.44.
Background
The current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable.
Results
An automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed.
Conclusion
The technical realization of “Phenomenon” allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time.
Semi-natural grasslands (SNGs) are an essential part of European cultural landscapes. They are an important habitat for many animal and plant species and offer a variety of ecological functions. Diverse plant communities have evolved over time depending on environmental and management factors in grasslands. These different plant communities offer multiple ecosystem services and also have an effect on the forage value of fodder for domestic livestock. However, with increasing intensification in agriculture and the loss of SNGs, the biodiversity of grasslands continues to decline. In this paper, we present a method to spatially classify plant communities in grasslands in order to identify and map plant communities and weed species that occur in a semi-natural meadow. For this, high-resolution multispectral remote sensing data were captured by an unmanned aerial vehicle (UAV) in regular intervals and classified by a convolutional neural network (CNN). As the study area, a heterogeneous semi-natural hay meadow with first- and second-growth vegetation was chosen. Botanical relevés of fixed plots were used as ground truth and independent test data. Accuracies up to 88% on these independent test data were achieved, showing the great potential of the usage of CNNs for plant community mapping in high-resolution UAV data for ecological and agricultural applications.