Occurrence Prediction of Pests and Diseases in Rice of Weather Factors Using Machine Learning

Dubey, Sachit; Barskar, Raju; Deen, Anjna Jayant; Barskar, Nepal; Ahmed, Gulfishan Firdose

doi:10.1007/978-981-33-6981-8_17

Cited by 2 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was observed that ANN algorithm shows an accuracy of 90% for blast infected image and 88% accuracy for normal image while KNN showed an accuracy of 79% for blast infected images and 63% for normal images. Dubey [37] evaluated the accuracy of different machine learning techniques in predicting occurrence of pests and diseases associated with rice using weather data. The weather and pest data was taken from the Crop Pest DSS database.…”

Section: Introductionmentioning

confidence: 99%

A Review on Machine Learning Techniques Used in Rice Crop Management

Das¹,

Baruah²

2022

IJRASET

View full text Add to dashboard Cite

Rice is a major food crop for a significant population of the world and increase in its yield is a priority to ensure nutrition for many countries. Thus it is important to protect the crop from the various diseases that affect the crop. With the advent of machine learning, a number of methods have been proposed to identify diseases after the occurrence as well as at an early stage. Diagnose of these predators before occurrence can be much more beneficial. Journal papers that used rice as a major crop were considered eligible. In this review, an attempt is made to understand the preferred machine learning methods and algorithms that can be employed to detect various diseases and pest as well as predict crop yield of the rice crop. It is expected to be beneficial to all who wish to use machine learning in agriculture that can lead to constructive research in this area.

show abstract

Section: Introductionmentioning

confidence: 99%