Crop Health Detector Development Using Q-Learning and Afta - A Survey

The paper talks about the usage of ICT technologies in the field of farming. The various usages of Iot devices, machine learning algorithms, robotics and artificial intelligence are studied and a detailed review is taken of all the devices with their pros and cons in both their theoretical usage and practical usage. Additionally the usage of drones are also studied in the application of farming practices. We use the available technologies for the benefit of the growth of the crop. We use this particular technologies in the detection of crop diseases. Crop diseases have to be detected in the early stages to reduce significant damage during yield. For this purpose we use soil moisture sensor, DHT-11, Raspberry pi, camera module. We insert this system with an algorithm that Is a combination of both Q-Learning and Active Federated Transfer Algorithm. These models are combined and formed a hybrid model that can be beneficial and overcome the issues that have been faced while using the conventional models of Convolutional neural networks and deep learning methods. The Q- learning models will typically share their data to a central server which puts together to form a knowledge base. The global data then is structured as to find and finetuned to form a better version of the local data. Local data is then sent to the farm where it can be incorporated to form with Q-Learning processes and a wide range of disease detection strategies can be devised to stop the spread.