A method for durian precise fertilization based on improved radial basis neural network algorithm.

Introduction: Durian is one of the tropical fruits that requires soil nutrients in its cultivation. It is important to understand the relationship between the content of critical nutrients, such as nitrogen (N), phosphorus (P), and potassium (K) in the soil and durian yield. How to optimize the fertilization plan is also important to the durian planting. Methods: Thus, this study proposes an Improved Radial Basis Neural Network Algorithm (IM-RBNNA) in the durian precision fertilization. It uses the gray wolf algorithm to optimize the weights and thresholds of the RBNNA algorithm, which can improve the prediction accuracy of the RBNNA algorithm for the soil nutrient content and its relationship with the durian yield. It also collects the soil nutrients and historical yield data to build the IM-RBNNA model and compare with other similar algorithms. Results: The results show that the IM-RBNNA algorithm is better than the other three algorithms in the average relative error, average absolute error, and coefficient of determination between the predicted and true values of soil N, K, and P fertilizer contents. It also predicts the relationship between soil nutrients and yield, which is closer to the true value. Discussion: It shows that the IM-RBNNA algorithm can accurately predict the durian soil nutrient content and yield, which is benefited for farmers to make agronomic plans and management strategies. It uses soil nutrient resources efficiently, which reduces the environmental negative impacts. It also ensures that the durian tree can obtain the appropriate amount of nutrients, maximize its growth potential, reduce production costs, and increase yields.

Design of a data processing method for the farmland environmental monitoring based on improved Spark components.

With the popularization of big data technology, agricultural data processing systems have become more intelligent. In this study, a data processing method for farmland environmental monitoring based on improved Spark components is designed. It introduces the FAST-Join (Join critical filtering sampling partition optimization) algorithm in the Spark component for equivalence association query optimization to improve the operating efficiency of the Spark component and cluster. The experimental results show that the amount of data written and read in Shuffle by Spark optimized by the FAST-join algorithm only accounts for 0.958 and 1.384% of the original data volume on average, and the calculation speed is 202.11% faster than the original. The average data processing time and occupied memory size of the Spark cluster are reduced by 128.22 and 76.75% compared with the originals. It also compared the cluster performance of the FAST-join and Equi-join algorithms. The Spark cluster optimized by the FAST-join algorithm reduced the processing time and occupied memory size by an average of 68.74 and 37.80% compared with the Equi-join algorithm, which shows that the FAST-join algorithm can effectively improve the efficiency of inter-data table querying and cluster computing.