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A Mobile App for Detecting Potato Crop Diseases.

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This study developed a mobile app using deep neural networks to detect potato diseases like early blight and late blight with 98.7% accuracy. The offline app aids farmers in identifying crop health issues and provides disease information.

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Area of Science:

  • Agricultural technology
  • Plant pathology
  • Artificial intelligence in agriculture

Background:

  • Potato crops are susceptible to devastating diseases like early blight (Alternaria solani) and late blight (Phytophthora infestans).
  • Accurate and timely disease detection is crucial for effective crop management and yield preservation.
  • Artificial intelligence (AI) offers promising solutions for automated disease diagnosis in agriculture.

Purpose of the Study:

  • To develop and evaluate a mobile application for detecting potato diseases using deep neural networks.
  • To achieve high diagnostic accuracy for early blight and late blight in potato crops.
  • To provide farmers with an accessible, offline tool for crop health monitoring.

Main Methods:

  • Utilized the PlantVillage dataset comprising 1000 images per class (healthy, early blight, late blight).
  • Exploratory analysis of deep neural network architectures, specifically MobileNetv2, for disease diagnosis.
  • Developed an offline mobile application compatible with Android 4.1+ devices.

Main Results:

  • Achieved a diagnostic accuracy of 98.7% for early and late blight detection using MobileNetv2.
  • Successfully developed a functional offline mobile application for potato disease identification.
  • The application includes an informational section on 27 potato diseases and a symptom gallery.

Conclusions:

  • Deep neural networks, particularly MobileNetv2, are highly effective for diagnosing potato diseases like early and late blight.
  • The developed mobile application provides a practical and accurate tool for farmers to manage potato crop health.
  • Future work will focus on incorporating segmentation techniques for damaged region analysis and multi-disease identification.