Neural Networks for Handwritten English Alphabet Recognition

This paper demonstrates the use of neural networks for developing a system that can recognize hand-written English alphabets. In this system, each English alphabet is represented by binary values that are used as input to a simple feature extraction system, whose output is fed to our neural network system.Comment: 5 pages, 3 Figure, ISSN:0975 - 888

A Novel Approach for Crop Selection and Water Management using Mamdani’s Fuzzy Inference & IOT

In the modern world, technology is always evolving to replace more human labour with artificial intelligence. Moreover, farmers are under constant pressure to irrigate their farms at regular intervals without even a rudimentary grasp of the rainfall pattern and soil humidity, since it is extremely difficult to cultivate any agricultural food in regions with irregular rainfall patterns and high mean temperatures. This paper proposes a crop predictor and smart irrigation system using Mamdani’s fuzzy inference and IoT. The system aims to optimize water usage and crop yield by considering various factors such as soil moisture, temperature, humidity, rainfall, crop type and season. The system consists of three modules: a crop predictor module that uses fuzzy logic to suggest the best crop for a given location and season, an IOT module that collects and transmits the environmental data from sensors to a cloud server, and a smart irrigation module that uses fuzzy logic to control the water flow to the crops based on the data and the crop predictor module. The system is implemented and tested on a NodeMCU and MATLAB platform and shows promising results in terms of water conservation and crop productivity