Crop Disease Prediction and Management

EasyChair Preprint no. 12129

Abstract

Crop diseases have a significant global impact on agriculture, threatening food security. Detecting and managing these diseases early is crucial for safeguarding crop yields. This research paper introduces a holistic framework for crop disease detection and management, combining advanced image analysis, machine learning, and traditional farming practices. Its primary focus is on early disease identification and effective management. The approach utilizes diverse data sources such as field surveys, remote sensing, and mobile apps to identify disease symptoms, assess severity, and recommend management strategies. Case studies involving various crops showcase the framework's potential to improve crop health, promote agricultural sustainability, and enhance global food security.

In summary, this research paper presents a comprehensive framework for crop disease detection and management, integrating advanced image analysis, machine learning, and traditional farming practices. It addresses the crucial issue of early disease identification and efficient management by utilizing diverse data sources. Case studies illustrate its potential to enhance crop health, agricultural sustainability, and global food security by offering personalized disease management strategies while reducing reliance on broad-spectrum pesticides. This initiative signifies a significant step towards revolutionizing agriculture for a more sustainable food production future.

Keyphrases: Agriculture, Crop disease, deep learning, image analysis, prediction

@Booklet{EasyChair:12129,
  author = {Anand Kunal Mishra and Prithvi Singh Kirar and S Prabakeran},
  title = {Crop Disease Prediction and Management},
  howpublished = {EasyChair Preprint no. 12129},

  year = {EasyChair, 2024}}