Towards Precision Fertilization: Multi-Strategy Grey Wolf Optimizer Based Model Evaluation and Yield Estimation

Precision fertilization is a major constraint in consistently balancing the contradiction between land resources, ecological environment, and population increase. Even more, it is a popular technology used to maintain sustainable development. Nitrogen (N), phosphorus (P), and potassium (K) are the main sources of nutrient income on farmland. The traditional fertilizer effect function cannot meet the conditional agrochemical theory’s conditional extremes because the soil is influenced by various factors and statistical errors in harvest and yield. In order to find more accurate scientific ratios, it has been proposed a multi-strategy-based grey wolf optimization algorithm (SLEGWO) to solve the fertilizer effect function in this paper, using the “3414” experimental field design scheme, taking the experimental field in Nongan County, Jilin Province as the experimental site to obtain experimental data, and using the residuals of the ternary fertilizer effect function of Nitrogen, phosphorus, and potassium as the target function. The experimental results showed that the SLEGWO algorithm could improve the fitting degree of the fertilizer effect equation and then reasonably predict the accurate fertilizer application ratio and improve the yield. It is a more accurate precision fertilization modeling method. It provides a new means to solve the problem of precision fertilizer and soil testing and fertilization.

Granulometric Parameters of Solid Blueberry Fertilizers and Their Suitability for Precision Fertilization

For precise fertilization of blueberry plants, it is technologically the easiest and most suitable option to use a volumetric filling, for which it can be presumed that it is possible to precisely dose the fertilizer for each plant by grams. For setting up a volumetric filler, it is necessary to know parameters such as the size of the fertilizer particles and their bulk density. The aim of this research is to determine the granulometric parameters and their effect, which is done by measuring up three different fertilizers (SQM Qrop K, Memon Siforga, Substral): width, height, and length of 100 randomly selected fertilizer particles as well as the volumes and weights of 100 particles in 10 repetitions. According to the measurements, the average diameters of fertilizer particles were found as well as the average mass, volumes, and bulk density. A Mahr Digital Caliper 16EWRi 0–150 mm was used to measure the diameters of the fertilizer granules. A Yxlon FF35 computer tomograph was used to accurately scan particles. The analytical scale, Kern ABJ 220-4NM, was used to determine mass. The volumes were measured, using measuring glasses, with one having a maximum volume of 10 mL in 0.2 mL increments and another having a maximum volume of 100 mL in 1 mL increments. Descriptive statistics analysis was performed in Microsoft Excel. It turned out that the average diameters (3.68 vs. 3.64 vs. 4.29 mm) and bulk densities (0.928 vs. 0.631 vs. 0.824 g cm−3) of the three fertilizers differed far from each other, meaning that the given volume could be filled with different amounts of fertilizer. Equations between mass and weight were formed according to the measurements. As a result, it was found that a volumetric filler can be used for fertilizing blueberry plants precisely, but it demands adjusting the filler each time in the situation, which is defined by the variety of blueberry plants: their age, size, and health.

From Conventional to Precision Fertilization: A Case Study on the Transition for a Small-Medium Farm

At the CREA research facility of Treviglio (Bergamo, Italy), to provide farmers with valuable hints for the transition from conventional to precision agriculture, information on crop production dynamics (Maize and Triticale) has been obtained using real-time soil mapping (resistivity technique) and production quality and quantity monitoring with a commercial yield mapping apparatus. The geostatistical processing of data resulted in the same zoning for Triticale, meaning that the characteristics of soil influenced crop behavior more than the variability resulting from other factors, which suggests that improvements in product yields can be planned and achieved acting, for instance, on variable rate distribution of fertilizers. The importance of the acquired data can help farmers to manage factors that are external to their plots of land.

Structural Optimization and Performance Evaluation of Blocking Wheel-Type Screw Fertilizer Distributor

In order to solve the problem where the amount of screw fertilizer distributor can only be adjusted by rotating speed and poor fertilization uniformity at low rotational speeds, a blocking wheel-type screw fertilizer distributor was designed. Single factor and L9(34) orthogonal simulation tests based on EDEM software were carried out to optimize the distributor variables at a speed of 20 r/min. The bench verification test was built under the same conditions as the simulation tests to verify the results of the simulation. Finally, the bench performance tests were carried out to evaluate distributor performance. The results of simulation tests revealed that the minimum coefficient of variation of fertilization uniformity (CVFU) was 19.27%, with the structural parameter combination of the inner diameter (17 mm), pitch (45 mm), outlet distance (40 mm), and number of screw heads (1). The verification test results showed that the changing trend and values of the CVFU were almost the same as the simulation tests. The results of the performance test revealed that when the opening width of the blocking wheel was 10–30 mm and the rotation speed was 20–60 r/min, the amount of fertilizer per lap (FAPL) was in the range of 27.74–38.15 g/r; the maximum CVFU and the coefficient of variation of fertilization stability (CVFS) were 29.43% and 2.18%, respectively, which met the requirements of the industry standard. This research provides a good reference for optimizing the screw fertilizer distribution and for researchers in the field of precision fertilization.

The use of crop sensors for precise fertilization

The paper presents the results of research on the use of ISARIA and OptRx sensors for the analysis of plant optical properties for precision fertilization. It was found that using the ISARIA and OptRx sensors of plant optical properties, it was possible to detect differences in the development of spring wheat crops. Consumption of OptRx mineral fertilizers in 2016 using precision fertilization of spring wheat and using plant optical analysis sensors was 3.5% higher, and in 2017 and 2018, respectively, 1.1 and 3.5% lower than in conventional intensive technology. Consumption of ISARIA mineral fertilizers in 2016 and 2018 using sensors were 0.5 and 0.6%, respectively, higher, and in 2017 it was 4.6% lower than in conventional intensive technology.

A Prediction Model of Forest Preliminary Precision Fertilization Based on Improved GRA-PSO-BP Neural Network

The optimal amount of fertilizer application which was needed by the trees and the factors that influence the fertilization have an intricated nonlinear relationship. According to the problems that the traditional fertilization prediction model has, such as lacking of the scalability and practicality, this paper initiates an accurate fertilization prediction model that was based on the GRA-PSO-BP neural network which can make the accurate fertilization come true and improve the economic benefits of forest industry. This paper uses the GRA method to determine the input of the neural network as the site index and make the forest age, nutrient content of the advantage trees, biomass of the advantage trees, biomass of average trees, and target yield as the output numbers of the Actual amount of fertilizer applied. During the calculation process, the global particle swarm optimization algorithm is used to optimize the initial numbers and threshold numbers of BP neural network which build a phased GRA-PSO-BP accurate fertilization model. Compared with the prediction algorithm of full input variate that is based on the single BP neural network and the prediction algorithm of full input variate that is based on PSO-BP Neural Network, the GRA method can determine the key factors that influence the amount of fertilizer applied in different forest areas and modify the prediction model to improve the scalability and accuracy of the prediction and finally achieve the precision fertilization as the data of different forests updated, so we can see that the prediction result of this paper is more accurate. The result demonstrates that the GRA-PSO-BP neural network Segment fertilization model is more accurate than the traditional BP neural network and BP Neural Network that was optimized by the PSO algorithm, and specifically, the error of the predicted amount of fertilizer application and the actual amount of fertilizer application is less than 5%, which can effectively guide the fertilization in stages.