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The demand for biostimulants has been growing at an annual rate of 10 and 12.4% in Europe and Northern America, respectively. The beneficial effects of humic substances (HS) as biostimulants of plant growth have been well-known since the 1980s, and they can be supportive to a circular economy if they are extracted from different renewable resources of organic matter including harvest residues, wastewater, sewage sludge, and manure. This paper presents an overview of the scientific outputs on application methods of HS in different conditions. Firstly, the functionality of HS in the primary and secondary metabolism under stressed and non-stressed cropping conditions is discussed along with crop protection against pathogens. Secondly, the advantages and limitations of five different types of HS application under open-fields and greenhouse conditions are described. Key factors, such as the chemical structure of HS, application method, optimal rate, and field circumstances, play a crucial role in enhancing plant growth by HS treatment as a biostimulant. If we can get a better grip on these factors, HS has the potential to become a part of circular agriculture.

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SummaryFarmers have access to many data-intensive technologies to help them monitor and control weeds and pests. Data collection, data modelling and analysis, and data sharing have become core challenges in weed control and crop protection. We review the challenges and opportunities of Big Data in agriculture: the nature of data collected, Big Data analytics and tools to present the analyses that allow improved crop management decisions for weed control and crop protection. Big Data storage and querying incurs significant challenges, due to the need to distribute data across several machines, as well as due to constantly growing and evolving data from different sources. Semantic technologies are helpful when data from several sources are combined, which involves the challenge of detecting interactions of potential agronomic importance and establishing relationships between data items in terms of meanings and units. Data ownership is analysed using the ethical matrix method to identify the concerns of farmers, agribusiness owners, consumers and the environment. Big Data analytics models are outlined, together with numerical algorithms for training them. Advances and tools to present processed Big Data in the form of actionable information to farmers are reviewed, and a success story from the Netherlands is highlighted. Finally, it is argued that the potential utility of Big Data for weed control is large, especially for invasive, parasitic and herbicide-resistant weeds. This potential can only be realised when agricultural scientists collaborate with data scientists and when organisational, ethical and legal arrangements of data sharing are established.

Rumex obtusifolius is a common grassland weed that is hard to control in a non-chemical way. The objective of our research was to automate the detection of R. obtusifolius as a step towards fully automated mechanical control of the weed. We have developed a vision-based system that uses textural analysis to detect R. obtusifolius against a grass background. Image sections are classified as grass or weed using 2-D Fourier analysis. We conducted two experiments. In the first (laboratory) experiment, we collected 28 images containing R. obtusifolius and 28 images containing only grass. Between 23 and 25 of 28 images were correctly classified (82-89%) as showing R. obtusifolius; all grass images were correctly classified as such. In the second (field) experiment, a self-propelled platform was used to obtain five sequences of images of R. obtusifolius plants.We used the parameters that gave the best classification results in the first experiment. We found, after changing one of the algorithmÕs parameters in response to prevailing light conditions, that we were able to detect R. obtusifolius in each image of each sequence. The algorithm scans a ground area of 1.5 m 2 in 30 ms. We conclude that the algorithm developed is sufficiently fast and robust to eventually serve as a basis for a practical robot to detect and control R. obtusifolius in grassland.