## 2 Related Works  
Previous research suggested a novel extract approach for the optimized sub-set function [4]. Cultivation depending on the forms to be tracked for algorithm items, dependent on the vector machine, help with classification. The performance provided by these technologies can be observed to have a total accuracy of about 89.6 percentage points. The authors of suggested an idea focused on fuzzy sets. When fluidity occurs, the brightness amount should be assumed in-pixel in the images for the calculated degree [5]. Then, the ambiguity of the images is treated effectively in the fuzzy package, and IFSs in particular are processed. Following this, the activation of the segmentation can be calculated by the satellite, by which the number of unknown images captured can be decreased. Then, the segmentation of the deficit of the crops for the clustering technique will fuse an image, since it depends on the interval between the intuitionist fuzzy set. A previous study used a chart rice crop-based neural network algorithm and forecast the yield in the district of Terai [6]. Moreover, the authors of [7] evaluated the production, water output (WEE), output in precipitation usage (PUE), and net crop returns based on proven crop responses to the use of water by integrating the rotation of grass/broadleaf. Other authors emphasized the degree to which different environmental factors influence rainfall and also used decisions made on crop development, including the identification of diseases and crop selection [8]. Previous authors [9] conducted a comparative study of the GIS-based crop prediction interface machine-learning algorithms (CSPM). Furthermore, presented a professional knowledge support program for rice, coffee, and cocoa production, focused on the user's input, and external details, such as the position and environment, which will assist selection processes, tracking, surveillance, detection, pest prevention, and selection of fertilizer, among others [10]. In addition, presents the creation of an automated device that analyzes and provides advice to farmers on infected paddy photos [11]. The key aim of designing a system for classifying rice diseases is to simplify the detection and classification of rice diseases [12], which include vector supports and artificial neural networks. The crop forecast takes account of parameters such as precipitation quantity, minimum and maximum temperature, soil type, humidity, and the importance of soil pH. Data were obtained from Maharashtra's agriculture website. Data were broken into nine farming regions. Previous authors have also defined and organized data according to the relative importance of the key variables that affect the yield of sugarcane and created mathematical models for the prediction of the yield of sugarcane through the use of data mining techniques (DMs) [13]. To that end, the databases of several sucrose mills in Sao Paulo, Brazil, were analyzed using three DM techniques. Meteorological variables and plant management have been studied using the following approaches of DM: Forest random, raise, and vector assistance. The resulting models have been evaluated using an independent dataset. As a comparison, a random forest algorithm was used. The application of machine learning techniques in agriculture to the study of soil fertility [14] was discussed. Agriculture has long been one of the research fields of concern. This research is aimed at evaluating, classifying, and enhancing soil data according to different factors. Technical developments such as robotics and data processing have taken advantage of agricultural science. Data mining nowadays is used in large fields, and many off-shelf database mining products and domain-specific data mining applications sell software, but data mining is a comparatively new field of study in agricultural soil datasets. The vast volumes of data now virtually obtained in connection with plants can be processed and used.