A Next-Generation Device for Crop Yield Prediction Using IoT and Machine Learning

This paper introduces a next-generation device for crop yield prediction that utilizes IoT and machine learning technologies. The device was implemented and tested, and it was found to have a high level of accuracy in predicting crop yields. It is a combination of three different machine learning models: Artificial Neural Network (ANN), Fuzzy Logic, and Support Vector Machine (SVM). The IoT sensors in the device gather data on various environmental and soil conditions such as temperature, humidity, and soil moisture, which is then fed into the machine learning models. The ANN is used to analyze the sensor data and extract features, the Fuzzy Logic model is used to handle uncertainty in the data and make predictions, and the SVM model is used for classification. The device was tested on various crops and it was observed that the accuracy of the predictions was good and the results were comparable to other state-of-the-art techniques. This technology has the potential to revolutionize the way farmers manage their crops and improve crop yields. It can also be used for crop forecasting, crop monitoring, and precision agriculture. By providing accurate and real-time information about crop yields, this device could help farmers make better decisions about their crops and increase their overall productivity and profitability.