Abiotic stresses such as drought, extreme temperature, and salinity can negatively impact seed germination and plant growth and have become major limitations to crop production. Most crops are vulnerable to abiotic stress factors during their early growth phase, especially during seed germination and seedling emergence. Rapid crop seed germination and seedling establishment is known to provide competitive advantages over weeds and improve yields. Seed osmopriming is defined as a pre-sowing treatment in which seeds are soaked in osmotic solutions to undergo the first stage of germination, but radicle protrusion has not occurred. The process of osmopriming involves prior exposure of seeds in low-water-potential solutions. Osmopriming can generate a series of pre-germination metabolic activities, increase the antioxidant system activities, and prepare the seed for radicle protrusion. Polyethylene glycol (PEG) is a popular osmopriming agent that can alleviate the negative impacts of abiotic stresses. This review summarizes research findings on crop responses to seed priming with PEG under abiotic stresses. The challenges, limitations, and opportunities of using PEG for crop seed priming are discussed with the goal of providing insights into future research towards effective application of seed priming in crop production.

Alfalfa is the most extensively cultivated forage legume worldwide, and salinity constitutes the main environmental scourge limiting its growth and productivity. To unravel the potential molecular mechanism involved in salt tolerance in alfalfa, we accomplished a combined analysis of parallel reaction monitoring-based proteomic technique and targeted metabolism. Based on proteomic analysis, salt stress induced 226 differentially abundant proteins (DAPs). Among them, 118 DAPs related to the antioxidant system, including glutathione metabolism and oxidation-reduction pathways, were significantly up-regulated. Data are available via ProteomeXchange with identifier PXD017166. Overall, 107 determined metabolites revealed that the tricarboxylic acid (TCA) cycle, especially the malate to oxaloacetate conversion step, was strongly stimulated by salt stress. This leads to an up-regulation by about 5 times the ratio of NADPH/NADP⁺, as well as about 3 to 5 times in the antioxidant enzymes activities, including those of catalase and peroxidase and proline contents. However, the expression levels of DAPs related to the Calvin–Benson–Bassham (CBB) cycle and photorespiration pathway were dramatically inhibited following salt treatment. Consistently, metabolic analysis showed that the metabolite amounts related to carbon assimilation and photorespiration decreased by about 40% after exposure to 200 mM NaCl for 14 d, leading ultimately to a reduction in net photosynthesis by around 30%. Our findings highlighted also the importance of the supplied extra reducing power, thanks to the TCA cycle, in the well-functioning of glutathione to remove and scavenge the reactive oxygen species (ROS) and mitigate subsequently the oxidative deleterious effect of salt on carbon metabolism including the CBB cycle.

Background: Precision spraying of postemergence herbicides according to the herbicide weed control spectrum can substantially reduce herbicide input. The objective of this research was to evaluate the effectiveness of using deep convolutional neural networks (DCNNs) for detecting and discriminating weeds growing in turfgrass based on their susceptibility to ACCase-inhibiting and synthetic auxin herbicides. Results: GoogLeNet, MobileNet-v3, ShuffleNet-v2, and VGGNet were trained to discriminate the vegetation into three categories based on the herbicide weed control spectrum: weeds susceptible to ACCase-inhibiting herbicides, weeds susceptible to synthetic auxin herbicides, and turfgrass without weed infestation (no herbicide). ShuffleNet-v2 and VGGNet showed high overall accuracy (≥ 0.999) and F₁ scores (≥ 0.998) in the validation and testing datasets to detect and discriminate weeds susceptible to ACCase-inhibiting and synthetic auxin herbicides. The inference time of ShuffleNet-v2 was similar to MobileNet-v3, but noticeably faster than GoogLeNet and VGGNet. ShuffleNet-v2 was the most efficient and reliable model among the neural networks evaluated. Conclusion: These results demonstrated that the DCNNs trained based on the herbicide weed control spectrum could detect and discriminate weeds based on their susceptibility to selective herbicides, allowing the precision spraying of particular herbicides to susceptible weeds and thereby saving more herbicides. The proposed method can be used in a machine vision-based autonomous spot-spraying system of smart sprayers.

Spot spraying POST herbicides is an effective approach to reduce herbicide input and weed control cost. Machine vision detection of grass or grass-like weeds in turfgrass systems is a challenging task due to the similarity in plant morphology. In this work, we explored the feasibility of using image classification with deep convolutional neural networks (DCNN), including AlexNet, GoogLeNet, and VGGNet, for detection of crabgrass species (Digitaria spp.), doveweed [Murdannia nudiflora (L.) Brenan], dallisgrass (Paspalum dilatatum Poir.), and tropical signalgrass [Urochloa distachya (L.) T.Q. Nguyen] in bermudagrass [Cynodon dactylon (L.) Pers.]. VGGNet generally outperformed AlexNet and GoogLeNet in detecting selected grassy weeds. For detection of P. dilatatum, VGGNet achieved high F1 scores (≥0.97) and recall values (≥0.99). A single VGGNet model exhibited high F1 scores (≥0.93) and recall values (1.00) that reliably detected Digitaria spp., M. nudiflora, P. dilatatum, and U. distachya. Low weed density reduced the recall values of AlexNet at detecting all weed species and GoogLeNet at detecting Digitaria spp. In comparison, VGGNet achieved excellent performances (overall accuracy = 1.00) at detecting all weed species in both high and low weed-density scenarios. These results demonstrate the feasibility of using DCNN for detection of grass or grass-like weeds in turfgrass systems.