Objectives:1-To develop an IoT enabled module to take soil moisture data and send it to a central database for analysis2-To design a Predictive Model to estimate the conditional probabilities of diseases affecting a crop using MLApproach:Developed a high accuracy (96%) IoT enabled wireless module for soil data monitoring using Capacitive SensorsProgrammed ESP and Arduino Nano to facilitate data transfer from the field to the central databaseEmployed a variety of Feature Selection, Pre-processing, and Data Sampling techniques for the predictive modelUtilized GridSearchCV for the hyper-parameter tuning of multi-class classifiers and improved the accuracy to 74%Impact of IoT Module:Affordability: Reduced the cost of the module from Rs 30,000 to less than Rs 1000 to make it affordable for an Indian farmerPortability: Reduced the volume of the module by 93% from 12,500 cm3 to 900 cm3 for easier mobilityUtility: The module facilitates precise recommendation of irrigation pattern and agricultural data logging Impact of Disease Prediction Model:Utility: The model predicted a disease in the crop, which on verifying from the field was found to be true