Int J Performability Eng ›› 2023, Vol. 19 ›› Issue (8): 491-498.doi: 10.23940/ijpe.23.08.p1.491498

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Real-Time Crop Disease Detection and Remedial Suggestion through Deep Learning-based Smartphone Application

Kavita Pandeya, and Dhiraj Pandeyb*()   

  1. aJaypee Institute of Information Technology, Noida, India 
    bJSS Academy of Technical Education, Noida, India
  • Contact: Dhiraj Pandey
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More than half of the workforce of many countries, such as India, are still engaged majorly in agriculture, according to a survey. Crop diseases are a major threat to food security that farmers grow every year. The early identification of crop disease remains difficult in many parts of India due to the lack of the necessary infrastructure. Several solutions have been devised at the governmental level to address the challenge of food security. Still, most Indian farmers do not have sufficient technical support to address major problems like monitoring fields, which includes irrigation control, soil moisture, invigilating water level, and detection of crop diseases. A solution in an affordable form that satisfies the Indian context is highly needed. In this article, the issue of crop disease detection has been addressed using the advanced technologies that can be provided in low-cost smartphones. Timely identification of diseases and subsequent immediate remedial action will help in saving the yields which automatically saves the economy of the farmer and in turn can help several farmers from distress. A deep learning-based real-time solution has been proposed that ensures ease of access, convenient architecture, and 24*7 connectivity by empowering the user with the element of Disease Prediction and Remedy suggestion.

Key words: deep learning, Crop Disease, disease detection, smartphone application, agriculture, prediction system