This work provides an edge slotted patch antenna in which a normal rectangular patch has been considered and slots have been introduced at the edge of the patch. The comparison of parameters such as $S_{11}$, VSWR have been done after implementing the slots. 11 GHz frequency is considered as design frequency for proposed patch. Resonant frequency after slot implementation has been found to be 11 GHz with −10 dB bandwidth of 1 GHz with antenna gain of 4.5 dB. Four slots of varying width have been placed on the proposed patch. Three different machine learning algorithms have been utilized for slot width optimization and parameters like MAE, MSE, RMSE and $\mathbf{R}^{\mathbf{2}}$ have been evaluated. The suggested antenna can be employed for 11 GHz applications that includes point-to-point communication and 6G. FR4 Epoxy material is chosen as substrate material.

Agriculture is the most important aspect of nation development. If the citizens of a nation have ample food then it not only helps them to work efficiently but also contributes to the growth of farmers. In developing nations like India, the demand for crops is rising. As countries like India and China are densely populated, the need for food is high and it increases day by day. The need for crop recommendation is due to less availability of land for farming. If the land for agriculture is limited then it is very essential to identify whether the selected crop is suitable for farming. Supervised machine learning algorithms are used for crop recommendation. Generally, classification techniques like Support Vector Classification (SVC), Random Forest ensemble algorithm, and Decision Trees Classifiers are used for crop recommendation. In this hybrid approach, we have used a stacking algorithm. The stacking algorithm combines the predictions of various machine learning algorithms. In our stacking algorithm, we have considered the meta-model and base model. The output of the first base model acts as the input for its respective meta-model. We have used the artificial neural network as the metamodel. We have proposed three stacking algorithms. In the first stacking algorithm, we have used Random Forest and Decision Tree Classification as the base model whereas the artificial neural network is the meta model. For the second model, we have combined Support Vector Machine (SVM) and Random Forest Classification as the base model and the artificial neural network as the meta-model. The third algorithm we proposed is the combination of a Decision Tree Classifier and SVM with a Decision Tree Classifier working as a meta-model. We obtained metrics like precision, accuracy, recall, and f-measure. The second stacking model gives the best results for the crop recommendation with 99.34% accuracy.

Crop production is crucial to the country's economic development by promoting sustainable agriculture. Integrating IoT and machine learning into crop recommendation systems widely contributes towards enhancing product yield, and minimizing farmers' efforts. The correct forecasting of crop recommendation may give a perfect outcome in analyzing the crop features based on its parameters. Objectives: This work aims to improve agricultural productivity using classification models. Our prime objective is to identify the specific parameters like soil productivity, humidity, wind velocity, rainfall, and other climatic conditions that affect the growth of crops. Classification-based prediction provides accurate results based on the optimality of crop characteristics. Materials and Methods: Our hybrid research model shows the efficiency of machine learning and IoT in the domain of crop recommendation systems. We have taken the real-time data of Nitrogen(N), Phosphorus(P), and Potassium(K) values of soil by using the NPK Soil Sensor. The real-time data collection was done at GIET University Agricultural Fields (School of Agriculture), India. The data is further converted into a. csv file for further processing. We have implemented five different types of classification techniques which include Gradient Boosting, Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine(SVM). Result Analysis: The effectiveness of all the different kinds of classification algorithms is assessed using the obtained results. We have estimated the performance metrics (accuracy, precision, F-measure, and recall) of all the classifiers.