Android Application for Crop Yield Prediction and Crop Disease Detection


Authors : Rushikesh Bhave, Kevin Bhalodia, Mayuresh Deodhar, Mansing Rathod.

Volume/Issue : Volume 3 - 2018, Issue 3 - March

Google Scholar : https://goo.gl/DF9R4u

Scribd : https://goo.gl/xuHPRr

Thomson Reuters ResearcherID : https://goo.gl/3bkzwv

Abstract : As we know, India’s economy primarily depends on agriculture. For successful production of crops we must ensure whether a particular crop will yield in particular soil and weather condition. And also if crop is not yielding properly, that means it must have some disease. So our project primarily focuses on two modules. One module will take Input factors such as weather condition, soil chemical content proportions, etc. and will use previous data in that condition and will give output as crop name which will be suitable in that particular condition. In second module application will take diseased crop image as an input and with the help of image mining we will predict which might be the possible disease caused to the crop. The proposed system represents a digital tool in the form of a mobile application, which will help farmers intelligently. It would include crop disease detection, crop yield prediction and recommendation of best crop as the prime focus. The prime focus is on improving the usability of agricultural services by providing a better tool.

Keywords : data mining; crop yield; usability; crop disease detection; recommendation of best crop.

As we know, India’s economy primarily depends on agriculture. For successful production of crops we must ensure whether a particular crop will yield in particular soil and weather condition. And also if crop is not yielding properly, that means it must have some disease. So our project primarily focuses on two modules. One module will take Input factors such as weather condition, soil chemical content proportions, etc. and will use previous data in that condition and will give output as crop name which will be suitable in that particular condition. In second module application will take diseased crop image as an input and with the help of image mining we will predict which might be the possible disease caused to the crop. The proposed system represents a digital tool in the form of a mobile application, which will help farmers intelligently. It would include crop disease detection, crop yield prediction and recommendation of best crop as the prime focus. The prime focus is on improving the usability of agricultural services by providing a better tool.

Keywords : data mining; crop yield; usability; crop disease detection; recommendation of best crop.

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