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.

Obtaining optimal wheat cultivars that balance high productivity and grain processing quality in diverse environmental and crop management conditions requires a comprehensive assessment of the influence of genetic and environmental factors and their interactions. This study investigated the influence of these factors on yield, grain quality, and bread-making traits in spring wheat (Triticum aestivum L.) cultivars. The study was conducted at four trial locations in the temperate climate area over two consecutive growing seasons, each with two different crop management approaches (moderate and high input). We observed a strong influence of genotype on grain quality (e.g., protein content, test weight) and farinographic in spring wheat. Environmental factors strongly influenced the variability of dough softening and quality number among the studied rheological traits. However, we observed that crop management significantly impacted dough stability. The strength of the relationships between yield, grain quality, and bread-making traits depended on the specific crop management used. The multi-trait stability of genotypes in yield, grain quality, and bread-making traits also varied, depending on the crop management method.

A field experiment was conducted in 2018–2020 to investigate the effect of two nitrogen application rates (N₀ = 0 kg ha⁻¹(control), N₉₀ = 90 kg ha⁻¹, and N₁₈₀ = 180 kg ha⁻¹) on morphological and physiological behaviours of two new rapeseed varieties; V₁ = JYJS01 (semi-dwarf variety) and V₂ = CY36 (tall variety). Our results indicated that N application enhanced the plant growth and seed protein content in comparison to control. Contrastingly, compared with control, sugar contents, i.e. sucrose and reducing sugars, decreased significantly in response to an increase in N. Seed oil content also decreased by 8.5% and 5.5%, 6.7% and 3.9% against N₁₈₀ in V₁ and V₂ during 2019 and 2020, respectively. Furthermore, higher nitrogen application rates decreased seed fatty acid proportions by decreasing the sugar availability for fatty acid biosynthesis. Our results demonstrated the highest seed yield (9.4 g plant⁻¹ in 2018/19 and 9.5 g plant⁻¹ in 2019/20) in V₂ against N₁₈₀, and the lowest seed yield (3.2 g plant⁻¹ in 2018/19 and 3.4 g plant⁻¹ in 2019/20) in V₁ at control. These findings imply that a high nitrogen application rate had increased the yield. At the same time, the carbohydrates translocation from stems to seed for fatty acid biosynthesis decreased, which played their significant role in seed physiology for fatty acid metabolisms, ultimately affected the seed quality. However, there is still a dire need to evaluate how nitrogen application affect the carbohydrate availability for fatty acid metabolism in the seed of new winter rapeseed varieties. Graphical abstract