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In the maize-soybean intercropping system, varying degrees of maize leaf shading are an important factor that reduces the uniformity of light penetration within the soybean canopy, altering the soybean canopy structure. Quantitative analysis of the relationship between the soybean canopy structure and canopy photosynthesis helps with breeding shade-tolerant soybean varieties for intercropping systems. This study examined the canopy structure and photosynthesis of intercropped soybeans during the shading stress period (28 days before the corn harvest), the high light adaptation period (15 days after the corn harvest), and the recovery period (35 and 55 days after the corn harvest), using a field high-throughput phenotyping platform and a plant gas exchange testing system (CAPTS). Additionally, indoor shading experiments were conducted for validation. The results indicate that shade-tolerant soybean varieties (STV varieties) have significantly higher yields than shade-sensitive soybean varieties (SSV varieties). This is attributable to the STV varieties having a larger top area, lateral width, and lateral external rectangular area. Compared to the SSV varieties, the four top areas of the STV varieties are, on average, 52.09%, 72.05%, and 61.37% higher during the shading stress, high light adaptation, and recovery periods, respectively. Furthermore, the average maximum growth rates (GRs) for the side mean width (SMW) and side rectangle area (SRA) of the STV varieties are 62.92% and 22.13% in the field, and 83.36% and 55.53% in the indoor environment, respectively. This results in a lower canopy overlap in STV varieties, leading to a more uniform light distribution within the canopy, which is reflected in higher photosynthetic rates (Pn), apparent quantum efficiency, and whole-leaf photosynthetic potential (WLPP) for the STV varieties, thereby enhancing their adaptability to shading stress. Above-ground dry matter accumulation was higher in STV varieties, with more assimilates stored in the source and sink, promoting assimilate accumulation in the grains. These results provide new insights into how the superior canopy structure and photosynthesis of shade-tolerant soybean varieties contribute to increased yield.
In the maize-soybean intercropping system, varying degrees of maize leaf shading are an important factor that reduces the uniformity of light penetration within the soybean canopy, altering the soybean canopy structure. Quantitative analysis of the relationship between the soybean canopy structure and canopy photosynthesis helps with breeding shade-tolerant soybean varieties for intercropping systems. This study examined the canopy structure and photosynthesis of intercropped soybeans during the shading stress period (28 days before the corn harvest), the high light adaptation period (15 days after the corn harvest), and the recovery period (35 and 55 days after the corn harvest), using a field high-throughput phenotyping platform and a plant gas exchange testing system (CAPTS). Additionally, indoor shading experiments were conducted for validation. The results indicate that shade-tolerant soybean varieties (STV varieties) have significantly higher yields than shade-sensitive soybean varieties (SSV varieties). This is attributable to the STV varieties having a larger top area, lateral width, and lateral external rectangular area. Compared to the SSV varieties, the four top areas of the STV varieties are, on average, 52.09%, 72.05%, and 61.37% higher during the shading stress, high light adaptation, and recovery periods, respectively. Furthermore, the average maximum growth rates (GRs) for the side mean width (SMW) and side rectangle area (SRA) of the STV varieties are 62.92% and 22.13% in the field, and 83.36% and 55.53% in the indoor environment, respectively. This results in a lower canopy overlap in STV varieties, leading to a more uniform light distribution within the canopy, which is reflected in higher photosynthetic rates (Pn), apparent quantum efficiency, and whole-leaf photosynthetic potential (WLPP) for the STV varieties, thereby enhancing their adaptability to shading stress. Above-ground dry matter accumulation was higher in STV varieties, with more assimilates stored in the source and sink, promoting assimilate accumulation in the grains. These results provide new insights into how the superior canopy structure and photosynthesis of shade-tolerant soybean varieties contribute to increased yield.
Large-scale yield estimation in the field or plot during wheat grain filling can contribute to high-throughput plant phenotyping and precision agriculture. To overcome the challenges of poor yield estimation at a large scale and for multiple species, this study employed a combination of multispectral and RGB drones to capture images and generation of time-series data on vegetation indices and canopy structure information during the wheat grubbing period. Five machine learning methods, partial least squares, random forest, support vector regression machine, BP neural networks, and long and short-term memory networks were used. The yield estimation of wheat grain filling period data was executed using a long and short-term memory network based on the preferred machine learning model, with a particular focus on distinguishing different heat-tolerant genotypes of wheat. The results unveiled a declining trend in the spectral reflectance characteristics of vegetation indices as the filling period progressed. Among the time-series data of the wheat filling period, the long and short-term memory network exhibited the highest estimation effectiveness, surpassing the BP neural network, which displayed the weakest estimation performance, by an impressive improvement in R2 of 0.21. The three genotypes of wheat were categorized into heat-tolerant genotype, moderate heat-tolerant genotype, and heat-sensitive genotype. Subsequently, the long and short-term memory network, which exhibited the most accurate yield estimation effect, was selected for regression prediction. The results indicate that the yield estimation effect was notably better than that achieved without distinguishing genotypes. Among the wheat genotypes, the heat-sensitive genotype demonstrated the most accurate prediction with an R2 of 0.91 and RMSE% of 3.25%. Moreover, by fusing the vegetation index with canopy structure information, the yield prediction accuracy (R2) witnessed an overall enhancement of about 0.07 compared to using the vegetation index alone. This approach also displayed enhanced adaptability to spatial variation. In conclusion, this study successfully utilized a cost-effective UAV for data fusion, enabling the extraction of canopy parameters and the application of a long and short-term memory network for yield estimation in wheat with different heat-tolerant genotypes. These findings have significant implications for informed crop management decisions, including harvesting and contingency forecasting, particularly for vast wheat areas.
Plant height is an important parameter of plant phenotype as one indicator of plant growth. In view of the complexity and scale limitation in current measurement systems, a scaleless method is proposed for the automatic measurement of plant height based on monocular computer vision. In this study, four peppers planted side by side were used as the measurement objects. Two color images of the measurement object were obtained by using a monocular camera at different shooting heights. Binary images were obtained as the images were processed by super-green grayscale and the Otsu method. The binarized images were transformed into horizontal one-dimensional data by the statistical number of vertical pixels, and the boundary points of multiple plants in the image were found and segmented into single-plant binarized images by filtering and searching for valleys. The pixel height was extracted from the segmented single plant image and the pixel displacement of the height was calculated, which was substituted into the calculation together with the reference height displacement to obtain the realistic height of the plant and complete the height measurements of multiple plants. Within the range of 2–3 m, under the light condition of 279 lx and 324 lx, this method can realize the rapid detection of multi-plant phenotypic parameters with a high precision and obtain more accurate plant height measurement results. The absolute error of plant height measurement is not more than ±10 mm, and the absolute proportion error is not more than ± 4%.
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