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A survey on plant disease prediction using machine learning and deep learning techniques

Authors

  • UshaDevi G Dr
  • gokulnath BV

Keywords:

Plant disease prediction, Crop productivity, Support vector machine, Deep learning, Meteorological factor, Visual symptoms, Random forest

Abstract

The major agricultural products in India are rice, wheat, pulses, and spices. As our population is increasing rapidly the demand for agriculture products also increasing alarmingly. A huge amount of data are incremented from various field of agriculture. Analysis of this data helps in predicting the crop yield, analyzing soil quality, predicting disease in a plant, and how meteorological factor affects crop productivity. Crop protection plays a vital role in maintaining agriculture product. Pathogen, pest, weed, and animals are responsible for the productivity loss in agriculture product. Machine learning techniques like Random Forest, Bayesian Network, Decision Tree, Support Vector Machine etc. help in automatic detection of plant disease from visual symptoms in the plant. A survey of different existing machine learning techniques used for plant disease prediction was presented in this paper. Automatic detection of disease in plant helps in early diagnosis and prevention of disease which leads to an increase in agriculture productivity.

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Published

2020-07-31

Versions

How to Cite

G, U., & BV, gokulnath. (2020). A survey on plant disease prediction using machine learning and deep learning techniques. Inteligencia Artificial, 23(65), 136-154. Retrieved from https://journal.iberamia.org/index.php/intartif/article/view/474

Issue

Section

Artificial Intelligece for Biomedical Informatics (closed)