Field application of allelopathic bacteria to control invasion of little seed canary grass in wheat

Harnessing Native Bacillus spp. for Sustainable Wheat Production

The genus Bacillus has been widely applied in contemporary agriculture as an environmentally-friendly biological agent. However, the real effect of commercial Bacillus-based fertilizers and pesticides varies immensely in the field. To harness Bacillus for efficient wheat production, we reviewed the diversity, functionality, and applicability of wheat-associated native Bacillus for the first time. Our main findings are: (i) Bacillus spp. inhabit the rhizosphere, root, stem, leaf, and kernel of wheat; (ii) B. subtilis and B. velezensis are the most widely endophytic species that can be isolated from both below and aboveground tissues; (iii) major functions of these representative strains are promotion of plant growth and alleviation of both abiotic and biotic stresses in wheat; (iv) stability and effectiveness are 2 major challenges during field application; (v) a STVAE pipeline that includes 5 processes, namely, Screen, Test, Validation, Application, and Evaluation, has been proposed for the capture and refinement of wheat-associated Bacillus spp. In particular, this review comprehensively addresses possible solutions, concerns, and criteria during the development of native Bacillus-based inoculants for sustainable wheat production.

Plant-Associated Bacteria as Sources for the Development of Bioherbicides

Weeds cause significant yield losses in crop production and influence the health of animals and humans, with some exotic weeds even leading to ecological crises. Weed control mainly relies on the application of chemical herbicides, but their adverse influences on the environment and food safety are a significant concern. Much effort has been put into using microbes as bioherbicides for weed control. As plant-associated bacteria (PAB), they are widely present in the rhizophere, inside crops or weeds, or as pathogens of weeds. Many species of PAB inhibit the seed germination and growth of weeds through the production of phytotoxic metabolites, auxins, hydrogen cyanide, etc. The performance of PAB herbicides is influenced by environmental factors, formulation type, surfactants, additives, application methods, and cropping measures, etc. These factors might explain the inconsistencies between field performance and in vitro screening results, but this remains to be clarified. Successful bioherbicides must be specific to the target weeds or the coinciding weeds. Detailed studies, regarding factors such as the formulation, application techniques, and combination with cultivation measures, should be carried out to maximize the performance of PAB-based bioherbicides.

Seed Germination Ecology of Semiparasitic Weed Pedicularis kansuensis in Alpine Grasslands

The semiparasitic weed Pedicularis kansuensis Maxim. has rapidly spread in the alpine grasslands of northern China over the past twenty years and has caused serious ecological problems. In order to effectively halt the spread of this weed, a thorough understanding of the dormancy type and the seed-germination ecology of P. kansuensis is required. We have conducted a series of experiments to investigate the effects of plant growth regulators (gibberellin (GA₃) and strigolactone synthesis (GR24)), as well as different abiotic (temperature, light, cold stratification, and drought) and biotic (aqueous extracts of three native dominant plants) factors on the seed-germination characteristics of P. kansuensis. The seed-germination percentages ranged from 2% to 62% at all of the temperatures that were examined, with the highest occurring at 25/10 °C. The light conditions did not significantly affect the germination percentage. The seed germination was greatly improved after two to eight weeks of cold stratification. The seed germination decreased dramatically with an increasing polyethylene glycol (PEG-6000) concentration, from 55% to 0%, under 10% and 20% PEG-6000. The seed germination was improved at a proper concentration of GA₃, GR24, and the aqueous extracts of Festuca ovina L., Stipa purpurea L., and Leymus secalinus (Georgi) Tzvel. Furthermore, in the pot experiment, the seedling emergence of P. kansuensis was also improved by the cultivation of these three dominant grasses. These findings indicate that the dormancy type of P. kansuensis seeds is non-deep physiological dormancy, and such findings will help in paving the way for the creation of effective weed management strategies, based on a thorough knowledge of germination ecology.

Weed Classification Using Explainable Multi-Resolution Slot Attention

In agriculture, explainable deep neural networks (DNNs) can be used to pinpoint the discriminative part of weeds for an imagery classification task, albeit at a low resolution, to control the weed population. This paper proposes the use of a multi-layer attention procedure based on a transformer combined with a fusion rule to present an interpretation of the DNN decision through a high-resolution attention map. The fusion rule is a weighted average method that is used to combine attention maps from different layers based on saliency. Attention maps with an explanation for why a weed is or is not classified as a certain class help agronomists to shape the high-resolution weed identification keys (WIK) that the model perceives. The model is trained and evaluated on two agricultural datasets that contain plants grown under different conditions: the Plant Seedlings Dataset (PSD) and the Open Plant Phenotyping Dataset (OPPD). The model represents attention maps with highlighted requirements and information about misclassification to enable cross-dataset evaluations. State-of-the-art comparisons represent classification developments after applying attention maps. Average accuracies of 95.42% and 96% are gained for the negative and positive explanations of the PSD test sets, respectively. In OPPD evaluations, accuracies of 97.78% and 97.83% are obtained for negative and positive explanations, respectively. The visual comparison between attention maps also shows high-resolution information.