Indian farmers are totally dependent on agriculture and livestock for satisfying their basic food and economical needs. Maximum farmers are habitual to take crops continuously with traditional ways without checking the current suitability. Government of India has developed centers to train and provide the information to farmers but everyonedon't approach to it. To get the increased yield, usually farmers add fertilizers without understanding requirement which may leads to soil degradation.Proposed solution is an automated system which can monitor major parameters required to estimate suitability for cropping. This system can be made available locally to every farmer. Outcome of this monitoring system can be used to identify particular crop suitability, so that suitable crop can be adopted.

Background: The key source of income in India is agriculture, so farming is called as backbone of Indian economy. To satisfy the need of increasing population increase in the crop yield is very important. India country programming framework stated that, the annual soil loss in India is about 5.3 billion tonnes. Methods: Majority farmers are small or marginal scale and are dependent on natural resources like soil-quality, rainfall and environmental condition etc. for their yield. Based on experience farmers decide which crop to be adopted. Government is arranging trainings and exhibitions to enhance the skillset of farmers. Result: A land which gives poor yield for one crop may produce adequate yield for some other crop/crops. To know the possible suitable crop/crops proposed machine learning model focuses current and potential suitability evaluation for available scenario.

Malaria disease is one whose presence is rampant in semi urban and non-urban areas especially resource poor developing countries. It is quite evident from the datasets like malaria, dengue, etc., where there is always a possibility of having more negative patients (non-occurrence of the disease) compared to patients suffering from disease (positive cases). Developing a model based decision support system with such unbalanced datasets is a cause of concern and it is indeed necessary to have a model predicting the disease quite accurately. Classification of imbalanced malaria disease data become a crucial task in medical application domain because most of the conventional machine learning algorithms are showing very poor performance to classify whether a patient is affected by malaria disease or not. In imbalanced data, majority (unaffected) class samples are dominates the minority (affected) class samples leading to class imbalance. To overcome the nature of class imbalance problem, balancing the data samples is the best solution which produces the better accuracy in classification of minority samples. The aim of this research is to propose a comparative study on classifying the imbalanced malaria disease data using Naive Bayesian classifier in different environments like weka and using an R-language. We present here, clinical descriptive study on 165 patients of different age group people collected at medical wards of Narasaraopet from 2014-17. Synthetic Minority Oversampling Technique (SMOTE) technique has been used to balance the class distribution and then we performed a comparative study on the dataset using Naïve Bayesian algorithm in various platforms. Out of balanced class distribution data, 70% data was given to train the Naive Bayesian algorithm and the rest of the data was used for testing the model for both weka and R programming environments. Experimental results have indicated that, classification of malaria disease data in weka environment has highest accuracy of 88.5% than the Naive Bayesian algorithm accuracy of 87.5% using R programming language. The impact of vector borne disease is very high in medical applications. Prediction of disease like malaria is an hour of the need and this is possible only with a suitable model for a given dataset. Hence, we have developed a model with Naive Bayesian algorithm is used for current research.

In recent years, agriculture solutions using robotics and the internet of things (IoT) technology provided advanced agricultural support for farmers to produce food products with the highest yields. However, a lack of effective and efficient agricultural device has resulted in the wide adoption of this IoT technique recently. This study presents an automatic seeding and fertiliser micro-dosing robot for farmers to achieve precision farming. The automatic robotic device for seed planting and micro-dosing of fertiliser is adapted to plant various types of seed and particularly well adapted to the plant seeds with micro-dosing fertiliser. The new option controls the number of seeds and quantifies the fertiliser quantity at each dropping point. It is also able to control the distance between each dropping point. This study has implemented the proof-of-concept prototype by the IoT system to demonstrate the feasibility of the agricultural outcomes. The experiments performed in the real-world environment which showed the effectiveness and efficiency of the device under different conditions.

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