Can deep fertilization in spring maize fields improve soil properties and their distribution in soil profile?

Deep fertilization strategy has been proven to be an important fertilizer management method for improving fertilizer utilization efficiency and crop yield. However, the relationship between soil chemical and biochemical characteristics and crop productivity under different fertilization depth patterns still needs comprehensive evaluation. Field tests on spring maize were therefore carried out in the Loess Plateau of China for two successive growing seasons from 2019 to 2020. Four distinct fertilization depths of 5 cm, 15 cm, 25 cm, and 35 cm were used to systematically investigate the effects of fertilization depth on soil physicochemical parameters, enzyme activity, and biochemical properties. The findings demonstrated that although adjusting fertilization depths (D15, D25) did not significantly affect the soil organic carbon content, they did significantly improve the soil chemical and biochemical characteristics in the root zone (10-30 cm), with D25 having a greater influence than D15. Compared with D5, the total nitrogen (TN), total phosphorus (TP), available nitrogen (AN), Olsen-P, dissolved organic carbon, and nitrogen (DOC and DON) in the root zone of D25 significantly increased by 12.02%, 7.83%, 22.21%, 9.56%, 22.29%, and 26.26%, respectively. Similarly, the urease, invertase, phosphatase, and catalase in the root zone of D25 significantly increased by 9.56%, 13.20%, 11.52%, and 18.05%, while microbial biomass carbon, nitrogen, and phosphorus (MBC, MBN, and MBP) significantly increased by 18.91%, 32.01% and 26.50%, respectively, compared to D5. By optimizing the depth of fertilization, the distribution ratio of Ca2-P and Ca8-P in the inorganic phosphorus components of the root zone can also be increased. Therefore, optimizing fertilization depth helps to improve soil chemical and biochemical characteristics and increase crop yield. The results of this study will deepen our understanding of how fertilization depth influence soil properties and crop responses.

Automated Counting Grains on the Rice Panicle Based on Deep Learning Method.

Grain number per rice panicle, which directly determines grain yield, is an important agronomic trait for rice breeding and yield-related research. However, manually counting grains of rice per panicle is time-consuming, laborious, and error-prone. In this research, a grain detection model was proposed to automatically recognize and count grains on primary branches of a rice panicle. The model used image analysis based on deep learning convolutional neural network (CNN), by integrating the feature pyramid network (FPN) into the faster R-CNN network. The performance of the grain detection model was compared to that of the original faster R-CNN model and the SSD model, and it was found that the grain detection model was more reliable and accurate. The accuracy of the grain detection model was not affected by the lighting condition in which images of rice primary branches were taken. The model worked well for all rice branches with various numbers of grains. Through applying the grain detection model to images of fresh and dry branches, it was found that the model performance was not affected by the grain moisture conditions. The overall accuracy of the grain detection model was 99.4%. Results demonstrated that the model was accurate, reliable, and suitable for detecting grains of rice panicles with various conditions.

Simulation analysis of fertilizer discharge process using the Discrete Element Method (DEM).

Fertilizer discharge process is a critical part of fertilizer application, as it affects the fertilizer discharge rate and uniformity of fertilizer application. In this study, a spiral grooved-wheel fertilizer discharge device was designed to replace the conventional straight grooved-wheel. Comparisons of the fertilizer discharge performance of the two grooved-wheel types were performed through tests and simulations using the discrete element method (DEM). The discharge performance of the two discharge devices was assessed by measuring the discharge mass rate, discharge uniformity, and the falling velocity of the fertilizer particles. Results showed that under similar conditions, the fertilizer discharge mass rate of the spiral grooved-wheel was higher than that of the straight grooved-wheel. The fertilizer discharge uniformity of the spiral grooved-wheel was much better than that of the straight grooved-wheel. The average falling velocity of fertilizer particles through the discharge spout was higher under the spiral grooved-wheel. The relative errors between the test and simulation results for the discharge mass rates, discharge uniformity, and particle falling velocities of the spiral grooved-wheel were all less than 10%. The developed spiral grooved-wheel exhibited a better performance than the conventional straight grooved-wheel, in all the aspects examined. The results serve as a theoretical basis for guiding the design of high-performance fertilizer applicators.

A Plant Leaf Geometric Parameter Measurement System Based on the Android Platform.

Automatic and efficient plant leaf geometry parameter measurement offers useful information for plant management. The objective of this study was to develop an efficient and effective leaf geometry parameter measurement system based on the Android phone platform. The Android mobile phone was used to process and measure geometric parameters of the leaf, such as length, width, perimeter, and area. First, initial leaf images were pre-processed by some image algorithms, then distortion calibration was proposed to eliminate image distortion. Next, a method for calculating leaf parameters by using the positive circumscribed rectangle of the leaf as a reference object was proposed to improve the measurement accuracy. The results demonstrated that the test distances from 235 to 260 mm and angles from 0 to 45 degrees had little influence on the leafs' geometric parameters. Both lab and outdoor measurements of leaf parameters showed that the developed method and the standard method were highly correlated. In addition, for the same leaf, the results of different mobile phone measurements were not significantly different. The leaf geometry parameter measurement system based on the Android phone platform used for this study could produce high accuracy measurements for leaf geometry parameters.