Monitoring Plant Health and Detection of Plant Disease Using IoT and ML

EasyChair Preprint 7358

Abstract

Crop diseases represent a severe difficulty in agriculture, affecting each exceptional and amount of agriculture production.  Hence, it's essential to increase the plantation and also to increase the production of crops. It is essential to increase crops yield and productiveness, keeping track of plant diseases until its harvesting is a major requirement. In this paper, an automatic disease detection machine has evolved the usage of the Internet Of Things (IoT) and Machine Learning which monitor temperature, humidity, rainfall, and color via sensors on the usage of NodeMCU primarily based totally on variants in plant leaf health condition. By the usage of those parameter values presence of the plant, the disease is identified. Machine Learning algorithm-based image processing is used for detecting diseases in the early-stage and keeping tracking diseases in leaves this task can be attained by using an artificial Convolution Neural Network algorithm. In this article tomato crop diseases had been considered for the study. The gathered information stays uploaded to the internet server by way of an IoT platform for information processing. A Mobile Application is developed which facilitates to show the status of the parameters and ship SMS warnings and notifications.

Keyphrases: Convolution Neural Network, Internet of Things (IoT), Machine Learning (ML), NodeMCU, Sensors, Webserver, image processing, mobile application

@booklet{EasyChair:7358,
  author    = {J Chandan and D Latha and R Manisha and G R Kishore},
  title     = {Monitoring Plant Health and Detection of Plant Disease Using IoT and ML},
  howpublished = {EasyChair Preprint 7358},
  year      = {EasyChair, 2022}}