利用模糊塚本预测土壤肥力的农业应用

Devia Kartika, Wifra Safitri
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摘要

印度尼西亚是一个农业国,大部分人口务农。印尼是一个农业国,拥有广阔的农业用地和丰富多样的自然资源。根据 Sakernas BPS 2021 年的数据,印尼农民人数为 3,877 万人。在农业领域,土地是决定农业经营的一个非常重要的因素。根据土壤类型和地理位置的不同,每个地区的土壤肥力水平也不同。因此,土壤肥力是决定农业经营成败的因素之一。许多农民不了解土壤肥力,无法确定正确的种植类型。本研究旨在帮助人们确定土壤肥力参数,如(有机碳、P2O5 HCL、K2O HCL、KTK、碱饱和度、PH H2O),这些参数将通过基于 WEB 的应用程序使用塚本模糊法进行处理,以预测肥力。用于选择植物类型和选择合适土地的土壤。从农业中心获得的土地数据,将使用模糊法进行人工处理,并应用到系统中。因此,本研究的结果将提供土壤肥力状况,从用有机碳(0.81%)、pH H2O(5.38)、P2O5(2.32 ppm)、KTK(8.5)、K2O(50 ppm)、碱饱和度(50%)处理过的数据中,我们得到土壤肥力状况为 46.03,处于中等范围。同时,在已建成的系统中,得到的结果是 45.54,也在同一范围内,即中等。从得到的结果来看,人工搜索和系统得到的结果差别不大,估计它们的相似度达到了 97%。有了这个土壤肥力检测系统,可以提高土壤肥力的准确性,使预测土壤肥力变得更加容易。希望该系统能对农业部门产生影响。关键词:农业、土壤肥力、模糊冢本、网络
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Agriculture Application to Predict Soil Fertility with the Application of Fuzzy Tsukamoto
Indonesia is as an agricultural country, having majority of the population working in agriculture. Indonesia is an agricultural country that has extensive agricultural land and diverse and abundant natural resources. Based on Sakernas BPS 2021 data, Indonesian farmers numbered 38.77 million people. In the agricultural sector, land is a factor that plays a very important role in determining agricultural businesses. Where each region has different levels of soil fertility, depending on the type of soil and geographic location of an area. So, soil fertility is one of the determining factors for the success of agricultural businesses. Many farmers do not understand soil fertility in determining the right type of plant. This research aims to help people determine soil fertility parameters such as (C-Organic, P2O5 HCL, K2O HCL, KTK, Base Saturation, PH H2O), which will be processed using a WEB-based application using the Tsukamoto fuzzy method to predict fertility. Soil for selecting plant types and choosing the right land. From the land data obtained at the agricultural center, it is processed manually using the fuzzy method and applied to a system. Therefore, the results of this research will provide soil fertility status from data processed with C-organic (0.81%), pH H2O (5.38), P2O5 (2.32 ppm), KTK (8.5), K2O (50 ppm), Base Saturation (50%), we got a soil fertility status of 46.03, which is in the medium range. Meanwhile, in the system that has been built, the results obtained were 45.54, which is also in the same range, namely medium. From the results obtained, there was not much difference observed in the manual search and the system obtained results, it is estimated that they obtained 97% similarity. With this soil fertility detection system, it can increase the accuracy of soil fertility and make it easier to predict soil fertility. It is hoped that this system can have implications for the agricultural sector. Keywords: agriculture, soil fertility, Fuzzy Tsukomoto, Web
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