Biomass and crop height estimation of different crops using UAV-based LiDAR

Phenotyping of crops is important due to increasing pressure on food production. Therefore, an accurate estimation of biomass during the growing season can be important to optimize the yield. The potential of data acquisition by UAV-LiDAR to estimate fresh biomass and crop height was investigated for three different crops (potato, sugar beet, and winter wheat) grown inWageningen (The Netherlands) from June to August 2018. Biomass was estimated using the 3DPI algorithm, while crop height was estimated using the mean height of a variable number of highest points for each m2. The 3DPI algorithm proved to estimate biomass well for sugar beet (R2 = 0.68, RMSE = 17.47 g/m2) and winter wheat (R2 = 0.82, RMSE = 13.94 g/m2). Also, the height estimates worked well for sugar beet (R2 = 0.70, RMSE = 7.4 cm) and wheat (R2 = 0.78, RMSE = 3.4 cm). However, for potato both plant height (R2 = 0.50, RMSE = 12 cm) and biomass estimation (R2 = 0.24, RMSE = 22.09 g/m2), it proved to be less reliable due to the complex canopy structure and the ridges on which potatoes are grown. In general, for accurate biomass and crop height estimates using those algorithms, the flight conditions (altitude, speed, location of flight lines) should be comparable to the settings for which the models are calibrated since changing conditions do influence the estimated biomass and crop height strongly.</p

Automated airborne pest monitoring of drosophila suzukii in crops and natural habitats

Drosophila suzukii has become a serious pest in Europe attacking many soft-skinned crops such as several berry species and grapevines since its spread in 2008 to Spain and Italy. An efficient and accurate monitoring system to identify the presence of Drosophila suzukii in crops and their surroundings is essential for the prevention of damage to economically valuable fruit crops. Existing methods for monitoring Drosophila suzukii are costly, time and labor intensive, prone to errors, and typically conducted at a low spatial resolution. To overcome current monitoring limitations, we are developing a novel system consisting of sticky traps which are monitored by means of Unmanned Aerial Vehicles (UAVs) and an image processing pipeline that automatically identifies and counts the number of Drosophila suzukii per trap location. To this end, we are currently collecting high resolution RGB imagery of Drosophila suzukii flies in sticky traps taken from both a static position (tripod) and from a UAV, which are then used as input to train deep learning models. Preliminary results show that a large part of the of Drosophila suzukii flies that are caught in the sticky traps can be correctly identified by the trained deep learning models. In the future, an autonomously flying UAV platform will be programmed to capture imagery of the sticky traps under field conditions. The collected imagery will be transferred directly to cloud-based storage for subsequent processing and analysis to identify the presence and count of Drosophila suzukii in near real time. This data will be used as input to a decision support system (DSS) to provide valuable information for farmers

A Global Systematic Review of Improving Crop Model Estimations by Assimilating Remote Sensing Data: Implications for Small-Scale Agricultural Systems

There is a growing effort to use access to remote sensing data (RS) in conjunction with crop model simulation capability to improve the accuracy of crop growth and yield estimates. This is critical for sustainable agricultural management and food security, especially in farming communities with limited resources and data. Therefore, the objective of this study was to provide a systematic review of research on data assimilation and summarize how its application varies by country, crop, and farming systems. In addition, we highlight the implications of using process-based crop models (PBCMs) and data assimilation in small-scale farming systems. Using a strict search term, we searched the Scopus and Web of Science databases and found 497 potential publications. After screening for relevance using predefined inclusion and exclusion criteria, 123 publications were included in the final review. Our results show increasing global interest in RS data assimilation approaches; however, 81% of the studies were from countries with relatively high levels of agricultural production, technology, and innovation. There is increasing development of crop models, availability of RS data sources, and characterization of crop parameters assimilated into PBCMs. Most studies used recalibration or updating methods to mainly incorporate remotely sensed leaf area index from MODIS or Landsat into the WOrld FOod STudies (WOFOST) model to improve yield estimates for staple crops in large-scale and irrigated farming systems. However, these methods cannot compensate for the uncertainties in RS data and crop models. We concluded that further research on data assimilation using newly available high-resolution RS datasets, such as Sentinel-2, should be conducted to significantly improve simulations of rare crops and small-scale rainfed farming systems. This is critical for informing local crop management decisions to improve policy and food security assessments

Automated crop plant counting from very high-resolution aerial imagery

Knowing before harvesting how many plants have emerged and how they are growing is key in optimizing labour and efficient use of resources. Unmanned aerial vehicles (UAV) are a useful tool for fast and cost efficient data acquisition. However, imagery need to be converted into operational spatial products that can be further used by crop producers to have insight in the spatial distribution of the number of plants in the field. In this research, an automated method for counting plants from very high-resolution UAV imagery is addressed. The proposed method uses machine vision—Excess Green Index and Otsu’s method—and transfer learning using convolutional neural networks to identify and count plants. The integrated methods have been implemented to count 10 weeks old spinach plants in an experimental field with a surface area of 3.2 ha. Validation data of plant counts were available for 1/8 of the surface area. The results showed that the proposed methodology can count plants with an accuracy of 95% for a spatial resolution of 8 mm/pixel in an area up to 172 m2. Moreover, when the spatial resolution decreases with 50%, the maximum additional counting error achieved is 0.7%. Finally, a total amount of 170 000 plants in an area of 3.5 ha with an error of 42.5% was computed. The study shows that it is feasible to count individual plants using UAV-based off-the-shelf products and that via machine vision/learning algorithms it is possible to translate image data in non-expert practical information.</p

Sensing a Changing World

workshop “Sensing a Changing World” was held in Wageningen, The Netherlands, from November 19–21, 2008. The main goal of the workshop was to explore and discuss recent developments in sensors and (wireless) sensor networks for monitoring environmental processes and human spatial behavior in a changing world. The challenge is then to develop concepts and applications that can provide timely and on-demand knowledge to end-users in different domains over a range of different spatial and temporal scales. During this workshop over 50 participants, representing 15 countries, presented and discussed their recent research. The workshop provided a broad overview of state-of-the-art research in a broad range of application fields: oceanography, air quality, biodiversity and vegetation, health, tourism, water management, and agriculture. In addition the workshop identified the future research challenges. One of the outcomes of the workshop was a special issue in the journal Sensors with contributions presented at the workshop. This editorial of the special issue aims to provide an overview of the discussions held during the workshop. It highlights the ideas of the authors and participants of the workshop about directions of future research for further development of sensor-webs for “sensing” spatial phenomena. The “big” question was are we already able to sense a changing world? And if the answer is positive, then what are we going to sense and for what

Advancing environmental intelligence through novel approaches in soft bioinspired robotics and allied technologies: I-Seed project position paper for Environmental Intelligence in Europe

The EU-funded FET Proactive Environmental Intelligence project "I-Seed"(Grant Agreement n. 101017940, https://www.iseedproject.eu/) targets towards the development of a radically simplified and environmentally friendly approach for environmental monitoring. Specifically, I-Seed aims at developing a new generation of self-deployable and biodegradable soft miniaturized robots, inspired by the morphology and dispersion abilities of plant seeds, able to perform low-cost, environmentally responsible, in-situ measurements. The natural functional mechanisms of seeds dispersal offer a rich source of robust, highly adaptive, mass and energy efficient mechanisms, and behavioral and morphological intelligence, which can be selected and implemented for advanced, but simple, technological inventions. I-Seed robots are conceived as unique in their movement abilities because inspired by passive mechanisms and materials of natural seeds, and unique in their environmentally friendly design because made of all biodegradable components. Sensing is based on a chemical transduction mechanism in a stimulus-responsive sensor material with fluorescence-based optical readout, which can be read via one or more drones equipped with fluorescent LiDAR technology and a software able to perform a real time georeferencing of data. The I-Seed robotic ecosystem is envisioned to be used for collecting environmental data in-situ with high spatial and temporal resolution across large remote areas where no monitoring data are available, and thus for extending current environmental sensor frameworks and data analysis systems

Development of a Dynamic Web Mapping Service for Vegetation Productivity Using Earth Observation and in situ Sensors in a Sensor Web Based Approach

This paper describes the development of a sensor web based approach which combines earth observation and in situ sensor data to derive typical information offered by a dynamic web mapping service (WMS). A prototype has been developed which provides daily maps of vegetation productivity for the Netherlands with a spatial resolution of 250 m. Daily available MODIS surface reflectance products and meteorological parameters obtained through a Sensor Observation Service (SOS) were used as input for a vegetation productivity model. This paper presents the vegetation productivity model, the sensor data sources and the implementation of the automated processing facility. Finally, an evaluation is made of the opportunities and limitations of sensor web based approaches for the development of web services which combine both satellite and in situ sensor sources

The integration of empirical, remote sensing and modelling approaches enhances insight in the role of biodiversity in climate change mitigation by tropical forests

Tropical forests store and sequester high amounts of carbon and are the most diverse terrestrial cosystem. Studies show potentially important effects of biodiversity on carbon storage and equestration, but a complete understanding of this relationship across spatiotemporal scales relevant for climate change mitigation needs three approaches: empirical, remote sensing and ecosystem modelling. Here, we review the contribution of these individual approaches to the understanding of the relationship of biodiversity with carbon storage and sequestration, and find short-term and long term benefits of biodiversity at both broad and fine spatial scales. We argue that enhanced understanding is obtained by combining approaches, i.e., by using output from one approach to improve another approach and thus results in better input, validation and comparison between approaches. This can be further improved by integrating approaches through using ‘boundary objects’(i.e., variables) that can be understood and measured by all approaches, such as the diversity of leaf traits of the upper canopy and forest structure indices. Combining and especially integrating approaches will therefore lead to a better understanding of biodiversity effects on climate change mitigation. This is crucial for making sound policy decisions

Geosensors to Support Crop Production: Current Applications and User Requirements

Sensor technology, which benefits from high temporal measuring resolution, real-time data transfer and high spatial resolution of sensor data that shows in-field variations, has the potential to provide added value for crop production. The present paper explores how sensors and sensor networks have been utilised in the crop production process and what their added-value and the main bottlenecks are from the perspective of users. The focus is on sensor based applications and on requirements that users pose for them. Literature and two use cases were reviewed and applications were classified according to the crop production process: sensing of growth conditions, fertilising, irrigation, plant protection, harvesting and fleet control. The potential of sensor technology was widely acknowledged along the crop production chain. Users of the sensors require easy-to-use and reliable applications that are actionable in crop production at reasonable costs. The challenges are to develop sensor technology, data interoperability and management tools as well as data and measurement services in a way that requirements can be met, and potential benefits and added value can be realized in the farms in terms of higher yields, improved quality of yields, decreased input costs and production risks, and less work time and load