Early and high-throughput estimations of the crop harvest index (HI) are essential for crop breeding and field management in precision agriculture; however, traditional methods for measuring HI are time-consuming and labor-intensive. The development of unmanned aerial vehicles (UAVs) with onboard sensors offers an alternative strategy for crop HI research. In this study, we explored the potential of using low-cost, UAV-based multimodal data for HI estimation using red–green–blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors at 4 growth stages to estimate faba bean (Vicia faba L.) and pea (Pisum sativum L.) HI values within the framework of ensemble learning. The average estimates of RGB (faba bean: coefficient of determination [R²] = 0.49, normalized root-mean-square error [NRMSE] = 15.78%; pea: R² = 0.46, NRMSE = 20.08%) and MS (faba bean: R² = 0.50, NRMSE = 15.16%; pea: R² = 0.46, NRMSE = 19.43%) were superior to those of TIR (faba bean: R² = 0.37, NRMSE = 16.47%; pea: R² = 0.38, NRMSE = 19.71%), and the fusion of multisensor data exhibited a higher estimation accuracy than those obtained using each sensor individually. Ensemble Bayesian model averaging provided the most accurate estimations (faba bean: R² = 0.64, NRMSE = 13.76%; pea: R² = 0.74, NRMSE = 15.20%) for whole growth stage, and the estimation accuracy improved with advancing growth stage. These results indicate that the combination of low-cost, UAV-based multimodal data and machine learning algorithms can be used to estimate crop HI reliably, therefore highlighting a promising strategy and providing valuable insights for high spatial precision in agriculture, which can help breeders make early and efficient decisions.

Faba bean is an important member of legumes, which has richer protein levels and great development potential. Yield is an important phenotype character of crops, and early yield estimation can provide a reference for field inputs. To facilitate rapid and accurate estimation of the faba bean yield, the dual-sensor (RGB and multi-spectral) data based on unmanned aerial vehicle (UAV) was collected and analyzed. For this, support vector machine (SVM), ridge regression (RR), partial least squares regression (PLS), and k-nearest neighbor (KNN) were used for yield estimation. Additionally, the fusing data from different growth periods based on UAV was first used for estimating faba bean yield to obtain better estimation accuracy. The results obtained are as follows: for a single-growth period, S2 (12 July 2019) had the best accuracy of the estimation model. For fusion data from the muti-growth period, S2 + S3 (12 August 2019) obtained the best estimation results. Furthermore, the coefficient of determination (R²) values for RF were higher than other machine learning algorithms, followed by PLS, and the estimation effects of fusion data from a dual-sensor were evidently better than from a single sensor. In a word, these results indicated that it was feasible to estimate the faba bean yield with high accuracy through data fusion based on dual-sensor data and different growth periods.

Cyclic nucleotide-gated channels (CNGCs), non-selective cation channels localised on the plasmalemma, are involved in growth, development, and regulatory mechanisms in plants during adverse stress. To date, CNGC gene families in multiple crops have been identified and analysed. However, there have been no systematic studies on the evolution and development of CNGC gene families in legumes. Therefore, in the present study, via transcriptome analysis, we identified 143 CNGC genes in legumes, and thereafter, classified and named them according to the grouping method used for Arabidopsis thaliana. Functional verification for disease stress showed that four GmCNGCs were specifically expressed in the plasmalemma during the stress process. Further, functional enrichment analysis showed that their mode of participation and coordination included inorganic ion concentration regulation inside and outside the membrane via the transmembrane ion channel and participation in stress regulation via signal transduction. The CNGC family genes in G. max involved in disease stress were also identified and physiological stress response and omics analyses were also performed. Our preliminary results revealed the basic laws governing the involvement of CNGCs in disease resistance in G. max, providing important gene resources and a theoretical reference for the breeding of resistant soybean.