Classification of Corn Stalk Lodging Resistance Using Equivalent Forces Combined with SVD Algorithm

Identification and estimation of lodging in bread wheat genotypes using machine learning predictive algorithms

BACKGROUND

Lodging or stem bending decreases wheat yield quality and quantity. Thus, the traits reflected in early lodging wheat are helpful for early monitoring to some extent. In order to identify the superior genotypes and compare multiple linear regression (MLR) with support vector regression (SVR), artificial neural network (ANN), and random forest regression (RF) for predicting lodging in Iranian wheat accessions, a total of 228 wheat accessions were cultivated under field conditions in an alpha-lattice experiment, randomized incomplete block design, with two replications in two cropping seasons (2018-2019 and 2019-2020). To measure traits, a total of 20 plants were isolated from each plot and were measured using image processing.

RESULTS

The lodging score index (LS) had the highest positive correlation with plant height (r = 0.78**), Number of nodes (r = 0.71**), and internode length 1 (r = 0.70**). Genotypes were classified into four groups based on heat map output. The most lodging-resistant genotypes showed a lodging index of zero or close to zero. The findings revealed that the RF algorithm provided a more accurate estimate (R2 = 0.887 and RMSE = 0.091 for training data and R2 = 0.768 and RMSE = 0.124 for testing data) of wheat lodging than the ANN and SVR algorithms, and its robustness was as good as ANN but better than SVR.

CONCLUSION

Overall, it seems that the RF model can provide a helpful predictive and exploratory tool to estimate wheat lodging in the field. This work can contribute to the adoption of managerial approaches for precise and non-destructive monitoring of lodging.

Field-based mechanical phenotyping of cereal crops to assess lodging resistance

Plant mechanical failure, also known as lodging, is the cause of significant and unpredictable yield losses in cereal crops. Lodging occurs in two distinct failure modes-stalk lodging and root lodging. Despite the prevalence and detrimental impact of lodging on crop yields, there is little consensus on how to phenotype plants in the field for lodging resistance and thus breed for mechanically resilient plants. This review provides an overview of field-based mechanical testing approaches to assess stalk and root lodging resistance. These approaches are placed in the context of future perspectives. Best practices and recommendations for acquiring field-based mechanical phenotypes of plants are also presented.

Diverse maize hybrids are structurally inefficient at resisting wind induced bending forces that cause stalk lodging

BACKGROUND

Stalk lodging (breaking of agricultural plant stalks prior to harvest) results in millions of dollars in lost revenue each year. Despite a growing body of literature on the topic of stalk lodging, the structural efficiency of maize stalks has not been investigated previously. In this study, we investigate the morphology of mature maize stalks to determine if rind tissues, which are the major load bearing component of corn stalks, are efficiently organized to withstand wind induced bending stresses that cause stalk lodging.

RESULTS

945 fully mature, dried commercial hybrid maize stem specimens (48 hybrids, ~ 2 replicates, ~ 10 samples per plot) were subjected to: (1) three-point-bending tests to measure their bending strength and (2) rind penetration tests to measure the cross-sectional morphology at each internode. The data were analyzed through an engineering optimization algorithm to determine the structural efficiency of the specimens.

CONCLUSIONS

Hybrids with higher average bending strengths were found to allocate rind tissue more efficiently than weaker hybrids. However, even strong hybrids were structurally suboptimal. There remains significant room for improving the structural efficiency of maize stalks. Results also indicated that stalks are morphologically organized to resist wind loading that occurs primarily above the ear. Results are applicable to selective breeding and crop management studies seeking to reduce stalk lodging rates.

A novel wheat lodging resistance evaluation method and device based on the thrust force of the stalks

Wind speed is the most essential factor causing wheat lodging. Accurate understanding of the wind speed characteristics at near-surface layer of wheat fields and its effect on lodging is the basis of objective evaluation of wheat lodging resistance. In this paper, the characteristics of wind speed at the near-surface layer of wheat fields and their impact on lodging were studied. A new device was proposed for directly measuring the critical thrust force of wheat population lodging resistance in the field based on the black box method. A novel wheat stem lodging resistance evaluation method/model was established based on the critical thrust force of wheat population stem lodging and the wind speed characteristics of field near-surface layer. The method used the lodging critical wind speed as the index of wheat lodging resistance, which was verified by wind tunnel and field experiment. The results showed that there was a significant positive correlation between the critical wind speed of wheat lodging resistance and its critical thrust force. The values of wheat canopy apparent roughness length, wind attack angle, ventilation coefficient and other wind field characteristics had important effects on the calculation of wheat lodging resistance critical wind speed. The method can eliminate bias when calculating wheat lodging resistance by considering only one or a few indicators and the results of field lodging evaluation were consistent with those of field lodging survey. The method is simple and can be used to assess the lodging resistance of wheat, select extension regions for wheat varieties, and evaluate lodging factors in the field.