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Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts of machine learning and has been widely used where advanced automation and control have been required. The trend has shifted from classical image processing and machine learning techniques to modern artificial intelligence (AI) and deep learning (DL) methods. Based on large training datasets and pre-trained models, DL-based methods have proven to be more accurate than previous traditional techniques. Machine vision has wide applications in agriculture, including the detection of weeds and pests in crops. Variation in lighting conditions, failures to transfer learning, and object occlusion constitute key challenges in this domain. Recently, DL has gained much attention due to its advantages in object detection, classification, and feature extraction. DL algorithms can automatically extract information from large amounts of data used to model complex problems and is, therefore, suitable for detecting and classifying weeds and crops. We present a systematic review of AI-based systems to detect weeds, emphasizing recent trends in DL. Various DL methods are discussed to clarify their overall potential, usefulness, and performance. This study indicates that several limitations obstruct the widespread adoption of AI/DL in commercial applications. Recommendations for overcoming these challenges are summarized.

Laser weeding may contribute to less dependency on herbicides and soil tillage. Several research and commercial projects are underway to develop robots equipped with lasers to control weeds. Artificial intelligence can be used to locate and identify weed plants, and mirrors can be used to direct a laser beam towards the target to kill it with heat. Unlike chemical and mechanical weed control, laser weeding only exposes a tiny part of the field for treatment. Laser weeding leaves behind only ashes from the burned plants and does not disturb the soil. Therefore, it is an eco-friendly method to control weed seedlings. However, perennial weeds regrow from the belowground parts after the laser destroys the aerial shoots. Depletion of the belowground parts for resources might be possible if the laser continuously kills new shoots, but it may require many laser treatments. We studied how laser could be used to destroy the widespread and aggressive perennial weed Elymus repens after the rhizomes were cut into fragments. Plants were killed with even small dosages of laser energy and stopped regrowing. Generally, the highest efficacy was achieved when the plants from small rhizomes were treated at the 3-leaf stage.

The massive use of herbicides since the 1950s has resulted in increasing problems with herbicideresistant weeds and pollution of the environment, including food, feed, and water. These side effects have resulted in political pressures to reduce herbicide application. The European Commission aims to reduce the use and risk of chemicals and more hazardous pesticides in the EU. Therefore, new weed control methods are in demand. Laser weeding might be an alternative to replace or supplement herbicides and other weed control methods in an Integrated Weed Management (IPM) strategy. This work aimed to investigate how increasing laser energy affected common weeds when the apical meristem was exposed to irradiation at the early stages of development. A 50 W thulium-doped fibre laser with a diameter of 2 mm and a wavelength of 2 µm was used. The highest efficacy of laser irradiation was achieved when the grass weed ( Alopecurus myosuroides ) had one leaf and the dicot species were at the cotyledon stage. There was a large difference between the species’ susceptibility to irradiation probably caused by differences in morphology and growth habit. At the 4-leaf stage, most of the species regrew after irradiation. Laser weeding may be a solution to replace or supplement other weed control methods in some crops, but in general the weeds must be irradiated when they are at the cotyledon to 2-leaf stage to avoid regrowth.

This study aims to improve the quality and quantity of winter wheat by using the potential of combining the use of cold plasma and waste biorefinery products for improving wheat yield. Plasma was applied by a radio frequency (RF) plasma reactor operated with air for 180 s and 50 W. The waste biorefinery products, including pyroligneous acid, biochar, and azolla compost, were used as plant nutrition. The effects of cold plasma treatment and waste biorefinery products were determined by measuring plant photosynthesis, grain yield, and content of chlorophyll, carotenoids, anthocyanin, protein, and starch. The experiment was conducted during the cropping seasons 2016−18 in a randomized complete block design with four replications. The combination of cold plasma and pyroligneous acid increased the grain yield up to 40.0%. The photosynthesis rate was improved up to 39.3%, and total chlorophyll content up to 48.3% in both years. Seed plasma treatment combined with biochar application increased the starch content by 36.8%. Adding azolla compost increased the protein content by 35.4%. Using seed plasma treatment with biochar increased the microbial biomass carbon by 16.0%. The application of plasma and azolla compost increased the microbial biomass nitrogen by 29.0%.

A prototype of a relatively cheap laser-based weeding device was developed and tested on couch grass (Elytrigia repens (L.) Desv. ex Nevski) mixed with tomatoes. Three types of laser were used (0.3 W, 1 W, and 5 W). A neural network was trained to identify the weed plants, and a laser guidance system estimated the coordinates of the weed. An algorithm was developed to estimate the energy necessary to harm the weed plants. We also developed a decision model for the weed control device. The energy required to damage a plant depended on the diameter of the plant which was related to plant length. The 1 W laser was not sufficient to eliminate all weed plants and required too long exposure time. The 5 W laser was more efficient but also harmed the crop if the laser beam became split into two during the weeding process. There were several challenges with the device, which needs to be improved upon. In particular, the time of exposure needs to be reduced significantly. Still, the research showed that it is possible to develop a concept for laser weeding using relatively cheap equipment, which can work in complicated situations where weeds and crop are mixed.

Weed control is necessary to ensure a high crop yield with good quality. Herbicide application and mechanical weeding are the most common methods worldwide. The use of herbicides has led to the increasing occurrence of herbicide-resistant weeds and unwanted contamination of the environment. Mechanical weed control harms beneficial organisms, increases the degradation of organic matter, may dry out the soil, and stimulate new cohorts of weed seeds to germinate. Therefore, there is a need to develop more sustainable weed control means. We suggest using small autonomous vehicles equipped with lasers as a sustainable alternative method. Laser beams are based on electricity, which can be produced from non-fossil fuels. Deep learning methods can be used to locate and identify weed and crop plants for targeting and delivery of laser energy with robotic actuators. Given the targeted nature of laser beams, the area exposed for weed control can be reduced substantially compared to commonly used weed control methods. Therefore, the risk of affecting non-target organisms is minimized, and the soil will be kept untouched in the field, avoiding triggering weed seeds to germinate. Small autonomous vehicles may have limited weeding capacity, and precautions need to be taken as reflections from the laser beam can be harmful to humans and animals. In this paper, we discuss the pros and cons of replacing or supplementing common used weed control methods with laser weeding. The ability to use laser weeding technology is relatively new and not yet widely practiced or commercially available. Therefore, we do not discuss and compare the costs of the various methods at this early stage of the development of the technology.

As part of conservation of plant genetic resources, long-term storage of seeds is highly relevant for genebanks. Here we present a systematic review and a meta-analysis of studies on seed longevity focusing on half-life (P50) under different storage conditions. Six studies were selected for the meta-analysis; in addition, a high number of additional references were included in the discussion of the results. The results show that under ambient conditions, half-life is short, from 5 to 10 years, while under more optimal conditions, which for orthodox seeds is at low humidity and low temperature, half-life is more in the 40−60 years range, although with large interspecies variation. Under long-term genebank conditions, with seeds dried to equilibrium and thereafter kept at minus 18−20°C in waterproof bags or jars, half-life can be twice or three times as long. In general, many of the grain legume seeds, as well as corn, common oat, and common barley are long-lived, while cereal rye, onion, garden lettuce, pepper, and some of the forage grasses are more short-lived. Conditions during maturation and harvesting influence longevity, and proper maturation and gentle handling are known to be of importance. Seed longevity models have been developed to predict final germination based on initial viability, temperature, humidity, storage time, and species information. We compared predicted germination to results from the long-term experiments. The predicted values were higher or much higher than the observed values, which demonstrate that something in the seed handling in the genebanks have not been optimal. Long-term studies are now available with data at least up to 60 years of storage. Our review shows that the knowledge and methodology developed for the conservation of plant genetic resources should also work for wild species of orthodox seed nature.

A wheat field was sprayed with a dosage of 1.1 kg a.i./ha Roundup PowerMax 10 days before harvest. The 1H Nuclear Magnetic Resonance (NMR) spectroscopy was used for the detection and quantification of the glyphosate (GLYP) in dried wheat spikelets, leaves, and stems. The quantification was done by the integration of the CH2-P groups doublet at 3.00 ppm with good linearity. The GLYP content varied between different samples and parts of the plant. On average, the largest content of herbicide was found in leaves (20.0 mg/kg), followed by stems (6.4 mg/kg) and spikelets (6.3 mg/kg). Our study shows that the 1H-NMR spectroscopy can be a rapid and reliable tool for GLYP detection and quantification in the field studies.

Background Quinoa ( Chenopodium quinoa Willd.) is an ancient grain crop that is tolerant to abiotic stress and has favorable nutritional properties. Downy mildew is the main disease of quinoa and is caused by infections of the biotrophic oomycete Peronospora variabilis Gaüm. Since the disease causes major yield losses, identifying sources of downy mildew tolerance in genetic resources and understanding its genetic basis are important goals in quinoa breeding. Results We infected 132 South American genotypes, three Danish cultivars and the weedy relative C. album with a single isolate of P. variabilis under greenhouse conditions and observed a large variation in disease traits like severity of infection, which ranged from 5 to 83%. Linear mixed models revealed a significant effect of genotypes on disease traits with high heritabilities (0.72 to 0.81). Factors like altitude at site of origin or seed saponin content did not correlate with mildew tolerance, but stomatal width was weakly correlated with severity of infection. Despite the strong genotypic effects on mildew tolerance, genome-wide association mapping with 88 genotypes failed to identify significant marker-trait associations indicating a polygenic architecture of mildew tolerance. Conclusions The strong genetic effects on mildew tolerance allow to identify genetic resources, which are valuable sources of resistance in future quinoa breeding.

Seed production is an important element of weed population dynamics, and weed persistence relies upon the soil seed bank. In 2017 and 2018, we studied the relationship between the aboveground dry biomass of common weed species and their seed production. Weeds were selected randomly in the fields, and we surrounded the plants with a porous net to collect shed seeds during the growth season. Just before crop harvest, weeds were harvested, the plants’ dry weights were measured, and the number of seeds retained on the weeds was counted. A linear relationship between the biomass and the number of seeds produced was estimated. This relationship was not affected by year for Avena spica-venti, Chenopodium album, Galium aparine, or Persicaria maculosa. Therefore, the data of the two seasons were pooled and analysed together. For Alopecurus myosuroides, Anagallis arvensis, Capsella bursa-pastoris, Geranium molle, Polygonum aviculare, Silene noctiflora, Sonchus arvensis, Veronica persica, and Viola arvensis, the relationship varied significantly between the years. In 2017, the growing season was cold and wet, and the slope of the regression lines was less steep than in the dry season in 2018 for most species. Capsella bursa-pastoris was the most prolific seed producer with the steepest slope.