Machine Learning for Detection and Prediction of Crop Diseases and Pests: A Comprehensive Survey
Iscte – Instituto Universitário de Lisboa · Instituto de Novas Tecnologias
Abstract
Considering the population growth rate of recent years, a doubling of the current worldwide crop productivity is expected to be needed by 2050. Pests and diseases are a major obstacle to achieving this productivity outcome. Therefore, it is very important to develop efficient methods for the automatic detection, identification, and prediction of pests and diseases in agricultural crops. To perform such automation, Machine Learning (ML) techniques can be used to derive knowledge and relationships from the data that is being worked on. This paper presents a literature review on ML techniques used in the agricultural sector, focusing on the tasks of classification, detection, and prediction of diseases and pests,…
Citation impact
- FWCI
- 46.40
- Percentile
- 100%
- References
- 81
Authors
3Topics & keywords
- Agriculture
- Productivity
- Identification (biology)
- Agricultural productivity
- Automation
- Computer science
- Agricultural engineering
- Quality (philosophy)
- Zero hunger