This study was aimed to investigate the effects of organic carbon and silicon fertilizers on the lodging resistance, yield, and economic performance of rapeseed. Two cultivars, namely Jayou (lodging-resistant) and Chuannongyou (lodging-susceptible), were selected to evaluate the effects of various fertilizer treatments on rapeseed culm morphology, lignin accumulation, and their relationships with their lodging resistance indices. The results showed that both organic carbon and silicon fertilizer applications increased the plant height, basal stem diameter, internode plumpness, and bending strength of rapeseed in both the studied years. The bending strength was significantly and positively correlated with the lodging resistance index and lignin content. It was found that both organic carbon and silicon fertilizers had improved the activities of lignin biosynthesis enzymes (phenylalanine ammonia-lyase, 4-coumarate:CoA ligase, cinnamyl alcohol dehydrogenase, and peroxiredoxins) and their related genes to increase lignin accumulation in the culm, which ultimately improved the lodging resistance. At the same time, the thickness of the stem cortex, vascular bundle area, and xylem area was increased, and the stem strength was improved. The effect of silicon fertilizer was better than that of organic carbon fertilizer, but there was no significant difference with the mixed application of silicon fertilizer and organic carbon fertilizer. Similarly, silicon fertilizer increased the number of pods, significantly increased the yield, and improved the economic benefit, while organic carbon fertilizer had no significant effect on the yield. Therefore, we believe that organic carbon and silicon fertilizer can improve the lodging resistance of rape stems by improving the lignin accumulation and the mechanical tissue structure. Still, the effect of silicon fertilizer is the best. Considering the economic benefits, adding silicon fertilizer can obtain more net income than the mixed application of silicon fertilizer and organic carbon fertilizer.

In the maize–soybean intercropping system, shade is the major chronic restraint that affects normal growth of soybean. Different spatial patterns of this system affect the microclimate of soybean through shading from maize plants. However, the negative impacts of shading stress can be mitigated by providing optimal ratios of different fertilizers. Therefore, to test this hypothesis, soybean plants were grown under different light conditions (normal light or shade) to evaluate the response to varying NH4+/NO3− ratios. Seeds of soybean (Glycine max L. cv. Nan‐99‐6) were grown in nutrient solution with a total concentration of 5 mM N using different NH4+/NO3− ratios (T0 = 0:0, T1 = 0:100, T2 = 25:75, T3 = 50:50 and T4 = 75:25) for 40 days in a greenhouse at PPFD 320.95 μmol m−2 s−1 (low light) or 967.53 μmol m−2 s−1 (normal light). Under low light, growth and photosynthesis of soybean seedlings significantly decreased as compared to normal light conditions. However, the optimal ratios of NH4+/ NO3− improved growth and photosynthesis of soybean seedlings under both light conditions. Our results indicated that soybean seedlings supplied with optimal NH4+/NO3− ratios (25:75 and 50:50) have maximum biomass yield, chlorophyll pigments, leaf gas exchange, photochemical activity and root growth as compared to low and high NH4+/NO3− ratios (T1 and T4). High ratios of NH4+/NO3− (T4) resulted in reduced plant growth due to nutrient accumulation in plant tissues; therefore, we suggest that optimal ratios of NH4+/NO3− (T2 and T3) can enhance the shade tolerance of soybean seedlings.

Leaf age is an important trait in the process of maize (Zea mays L.) growth. It is significant to estimate the seed activity and yield of maize by counting leaves. Detection and counting of the maize leaves in the field are very difficult due to the complexity of the field scenes and the cross-covering of adjacent seedling leaves. A method was proposed in this study for detecting and counting maize leaves based on deep learning with RGB images collected by unmanned aerial vehicles (UAVs). The Mask R-CNN was used to separate the complete maize seedlings from the complex background to reduce the impact of weeds on leaf counting. We proposed a new loss function SmoothLR for Mask R-CNN to improve the segmentation performance of the model. Then, YOLOv5 was used to detect and count the individual leaves of maize seedlings after segmentation. The 1005 field seedlings images were randomly divided into the training, validation, and test set with the ratio of 7:2:1. The results showed that the segmentation performance of Mask R-CNN with Resnet50 and SmoothLR was better than that with LI Loss. The average precision of the bounding box (Bbox) and mask (Mask) was 96.9% and 95.2%, respectively. The inference time of single image detection and segmentation was 0.05 s and 0.07 s, respectively. YOLOv5 performed better in leaf detection compared with Faster R-CNN and SSD. YOLOv5x with the largest parameter had the best detection performance. The detection precision of fully unfolded leaves and newly appeared leaves was 92.0% and 68.8%, and the recall rates were 84.4% and 50.0%, respectively. The average precision (AP) was 89.6% and 54.0%, respectively. The rates of counting accuracy for newly appeared leaves and fully unfolded leaves were 75.3% and 72.9%, respectively. The experimental results showed the possibility of current research on exploring leaf counting for field-grown crops based on UAV images.

Plants are exposed to several adverse environmental effects during their life span. Among them drought stress is one of the major threats to agricultural productivity. In order to survive in such unstable environment, plants have developed mechanisms through which they recognize the severity of the stress based on the incoming environmental stimuli. To combat the detrimental effects of drought, the plants use various strategies to modulate their physio-hormonal attributes. They can be modulated by shade and microbes, which process can enhances drought tolerance and reduce yield loss. Plant hormones, such as abscisic acid, auxin and ethylene have a major role in the shade- and microbe-associated improvement of drought tolerance through their effects on various metabolic pathways. In this process, the CLAVATA3/EMBRYOSURROUNDING REGION-RELATED 25 peptide has a major role due to its effect on ABA synthesis as shown in our regulatory model.

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