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

Abstract

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.

Analysis of the Coupling Characteristics of Water Resources and Food Security: The Case of Northwest China

“…Taking measures to reduce the regional gray-water footprint would not only help to indirectly reduce the demand for water resources of regional grain production but also help to promote regional ecological security. For example, on the basis of vigorously developing regional water-saving irrigation technology, we should improve the utilization efficiency of crop nitrogen fertilizers by implementing integrated water-fertilizer irrigation, precision fertilization and soil fertilization [58] to reduce the demand for chemical fertilizers for crop growth by developing organic agriculture [59], etc. (d) On the basis of the above measures, the region should also change the current business model of smallscale farmers to integrated and intelligent agriculture (integrating modern information technologies such as the Internet, the Internet of Things, Big Data, cloud computing and artificial intelligence with agriculture, so as to realize the new agricultural production mode of agricultural information perception, quantitative decision making, intelligent control, precise investment and personalized service), so as to reduce the blue-water footprint and gray-water footprint of crops and increase the green-water footprint.…”

Assessment and Comparison of Agricultural Technology Development under Different Farmland Management Modes: A Case Study of Grain Production, China

“…It was found that the amount of fertilizer used by farmers in China showed the obvious characteristics of early empirical fixed behavior habits [34,35]. African farmers, who are risk-averse, are more likely to adopt drought tolerance technologies, because it helps reduce their production risks [36,37].…”

Automatic Detection and Classification of Insects Using Hybrid FF-GWO-CNN Algorithm

“…Deep learning approaches based on Convolutional Neural Networks (CNN) [25][26][27][28] are widely utilized for efficient classification with increased accuracy and convergence speed. This classification approach is optimized by various statistical algorithms, among which Firefly (FF) [29] and Grey Wolf Optimization (GWO) [30] are utilized in this approach. The hybridization of GWO and FF enhances the CNN classifier's performance, resulting in improved accuracy and a high convergence rate.…”