ABSTRACT
Apple (Malus pumila Mill.) is one of the important economic crops in the arid areas of Xinjiang, China. For a long time, there has been a problem of high consumption but low yield in water and fertilizer management, prevent improvements in apple quality and yield. In this study, 5-year-old 'Royal Gala' apple trees in extremely arid areas of Xinjiang were used as experimental materials to carry out field experiments. considering 5 irrigation levels (W1, 30 mm; W2, 425 mm; W3, 550 mm; W4, 675 mm; W5, 800 mm) and 5 fertilization levels (F1, 280 kg·ha-1; F2, 360 kg·ha-1; F3, 440 kg·ha-1; F4, 520 kg·ha-1; F5, 600 kg·ha-1) under magnetoelectric water irrigation conditions. The results demonstrated that magnetoelectric water combined with the application of 675 mm irrigation amount and 520 kg·ha-1 fertilization amount was the most effective combination. These results occurred by increasing net photosynthetic rate of apple leaves, improved the quality of apples, increased apple yield, and promoted the improvement of water and fertilizer use efficiency. Additionally, the quadratic regression model was used to fit the response process of yield, IWUE and PFP to irrigation amount and fertilization amount, and the accuracy was greater than 0.8, indicating good fitting effects. The synergistic effect of water and fertilizer has a positive effect on optimizing apple water and fertilizer management. Principal component analysis showed that the magnetoelectric treatment combined water and fertilizer mainly affected apple yield, water and fertilizer use efficiency and vitamin C content related to quality. This study provides valuable guidance for improving water and fertilizer productivity, crop yield and quality in extreme arid areas of Xinjiang by using Magnetoelectric water irrigation.
ABSTRACT
Crop leaf length, perimeter, and area serve as vital phenotypic indicators of crop growth status, the measurement of which is important for crop monitoring and yield estimation. However, processing a leaf point cloud is often challenging due to cluttered, fluctuating, and uncertain points, which culminate in inaccurate measurements of leaf phenotypic parameters. To tackle this issue, the RKM-D point cloud method for measuring leaf phenotypic parameters is proposed, which is based on the fusion of improved Random Sample Consensus with a ground point removal (R) algorithm, the K-means clustering (K) algorithm, the Moving Least Squares (M) method, and the Euclidean distance (D) algorithm. Pepper leaves were obtained from three growth periods on the 14th, 28th, and 42nd days as experimental subjects, and a stereo camera was employed to capture point clouds. The experimental results reveal that the RKM-D point cloud method delivers high precision in measuring leaf phenotypic parameters. (i) For leaf length, the coefficient of determination (R2) surpasses 0.81, the mean absolute error (MAE) is less than 3.50 mm, the mean relative error (MRE) is less than 5.93%, and the root mean square error (RMSE) is less than 3.73 mm. (ii) For leaf perimeter, the R2 surpasses 0.82, the MAE is less than 7.30 mm, the MRE is less than 4.50%, and the RMSE is less than 8.37 mm. (iii) For leaf area, the R2 surpasses 0.97, the MAE is less than 64.66 mm2, the MRE is less than 4.96%, and the RMSE is less than 73.06 mm2. The results show that the proposed RKM-D point cloud method offers a robust solution for the precise measurement of crop leaf phenotypic parameters.