Waterlogging has negative effects on crop yield. Physiological and transcriptome data of two peanut cultivars [Zhongkaihua 1 (ZKH 1) and Huayu 39 (HY 39)] were studied under normal water supply and waterlogging stress for 5 or 10 days at the flowering stage. The results showed that the main stem height, the number of lateral branches, lateral branch length, and the stem diameter increased under waterlogging stress, followed by an increase in dry matter accumulation, which was correlated with the increase in the soil and plant analysis development (SPAD) and net photosynthetic rate (Pn) and the upregulation of genes related to porphyrin and chlorophyll metabolism and photosynthesis. However, the imbalance of the source–sink relationship under waterlogging was the main cause of yield loss, and waterlogging caused an increase in the sucrose and soluble sugar contents and a decrease in the starch content; it also decreased the activities of sucrose synthetase (SS) and sucrose phosphate synthetase (SPS), which may be due to the changes in the expression of genes related to starch and sucrose metabolism. However, the imbalance of the source–sink relationship led to the accumulation of photosynthate in the stems and leaves, which resulted in the decrease of the ratio of pod dry weight to total dry weight (PDW/TDW) and yield. Compared with ZKH 1, the PDW of HY 39 decreased more probably because more photosynthate accumulated in the stem and leaves of HY 39 and could not be effectively transported to the pod.

Helminthosporium leaf blotch (HLB) is a serious disease of wheat causing yield reduction globally. Usually, HLB disease is controlled by uniform chemical spraying, which is adopted by most farmers. However, increased use of chemical controls have caused agronomic and environmental problems. To solve these problems, an accurate spraying system must be applied. In this case, the disease detection over the whole field can provide decision support information for the spraying machines. The objective of this paper is to evaluate the potential of unmanned aerial vehicle (UAV) remote sensing for HLB detection. In this work, the UAV imagery acquisition and ground investigation were conducted in Central China on April 22th, 2017. Four disease categories (normal, light, medium, and heavy) were established based on different severity degrees. A convolutional neural network (CNN) was proposed for HLB disease classification. The experiments on data preprocessing, classification, and hyper-parameters tuning were conducted. The overall accuracy and standard error of the CNN method was 91.43% and 0.83%, which outperformed other methods in terms of accuracy and stabilization. Especially for the detection of the diseased samples, the CNN method significantly outperformed others. Experimental results showed that the HLB infected areas and healthy areas can be precisely discriminated based on UAV remote sensing data, indicating that UAV remote sensing can be proposed as an efficient tool for HLB disease detection.

Drought negatively affects the growth and yield of terrestrial crops. Seed priming, pre-exposing seed to a compound, could induce improved tolerance and adaptation to stress in germinated plants. To understand the effects and regulatory mechanism of seed priming with brassinosteroid (BR) on peanut plants, we treated seeds with five BR concentrations and examined dozens of physiological and biochemical features, and transcriptomic changes in leaves under well-watered and drought conditions. We found optimal 0.15 ppm BR priming could reduce inhibitions from drought and increase the yield of peanut, and priming effects are dependent on stage of plant development and duration of drought. BR priming induced fewer differentially expressed genes (DEGs) than no BR priming under well-watered condition. Drought with BR priming reduced the number of DEGs than drought only. These DEGs were enriched in varied gene ontologies and metabolism pathways. Downregulation of DEGs involved in both light perceiving and photosynthesis in leaves is consistent with low parameters of photosynthesis. Optimal BR priming partially rescued the levels of growth promoting auxin and gibberellin which were largely reduced by drought, and increased levels of defense associated abscisic acid and salicylic acid after long-term drought. BR priming induced many DEGs which function as kinase or transcription factor for signal cascade under drought. We proposed BR priming-induced regulatory responses will be memorized and recalled for fast adaptation in later drought stress. These results provide physiological and regulatory bases of effects of seed priming with BR, which can help to guide the framing improvement under drought stress.

Weed control is necessary in rice cultivation, but the excessive use of herbicide treatments has led to serious agronomic and environmental problems. Suitable site-specific weed management (SSWM) is a solution to address this problem while maintaining the rice production quality and quantity. In the context of SSWM, an accurate weed distribution map is needed to provide decision support information for herbicide treatment. UAV remote sensing offers an efficient and effective platform to monitor weeds thanks to its high spatial resolution. In this work, UAV imagery was captured in a rice field located in South China. A semantic labeling approach was adopted to generate the weed distribution maps of the UAV imagery. An ImageNet pre-trained CNN with residual framework was adapted in a fully convolutional form, and transferred to our dataset by fine-tuning. Atrous convolution was applied to extend the field of view of convolutional filters; the performance of multi-scale processing was evaluated; and a fully connected conditional random field (CRF) was applied after the CNN to further refine the spatial details. Finally, our approach was compared with the pixel-based-SVM and the classical FCN-8s. Experimental results demonstrated that our approach achieved the best performance in terms of accuracy. Especially for the detection of small weed patches in the imagery, our approach significantly outperformed other methods. The mean intersection over union (mean IU), overall accuracy, and Kappa coefficient of our method were 0.7751, 0.9445, and 0.9128, respectively. The experiments showed that our approach has high potential in accurate weed mapping of UAV imagery.

Leaf area index (LAI) is used to predict crop yield, and unmanned aerial vehicles (UAVs) provide new ways to monitor LAI. In this study, we used a fixed-wing UAV with multispectral cameras for remote sensing monitoring. We conducted field experiments with two peanut varieties at different planting densities to estimate LAI from multispectral images and establish a high-precision LAI prediction model. We used eight vegetation indices (VIs) and developed simple regression and artificial neural network (BPN) models for LAI and spectral VIs. The empirical model was calibrated to estimate peanut LAI, and the best model was selected from the coefficient of determination and root mean square error. The red (660 nm) and near-infrared (790 nm) bands effectively predicted peanut LAI, and LAI increased with planting density. The predictive accuracy of the multiple regression model was higher than that of the single linear regression models, and the correlations between Modified Red-Edge Simple Ratio Index (MSR), Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), and LAI were higher than the other indices. The combined VI BPN model was more accurate than the single VI BPN model, and the BPN model accuracy was higher. Planting density affects peanut LAI, and reflectance-based vegetation indices can help predict LAI.