Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild

Artzai Picon, Aitor Alvarez-Gila, Maximiliam Seitz, Amaia Ortiz-Barredo, Jone Echazarra, Alexander Johannes

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

374 Citas (Scopus)

Resumen

Fungal infection represents up to 50% of yield losses, making it necessary to apply effective and cost efficient fungicide treatments, whose efficacy depends on infestation type, situation and time. In these cases, a correct and early identification of the specific infection is mandatory to minimize yield losses and increase the efficacy and efficiency of the treatments. Over the last years, a number of image analysis-based methodologies have been proposed for automatic image disease identification. Among these methods, the use of Deep Convolutional Neural Networks (CNNs) has proven tremendously successful for different visual classification tasks. In this work we extend previous work by Johannes et al. (2017) with an adapted Deep Residual Neural Network-based algorithm to deal with the detection of multiple plant diseases in real acquisition conditions where different adaptions for early disease detection have been proposed. This work analyses the performance of early identification of three relevant European endemic wheat diseases: Septoria (Septoria triciti), Tan Spot (Drechslera triciti-repentis) and Rust (Puccinia striiformis & Puccinia recondita).
Idioma originalInglés
Páginas (desde-hasta)280-290
Número de páginas11
PublicaciónComputers and Electronics in Agriculture
Volumen161
DOI
EstadoPublicada - jun 2019

Palabras clave

  • Convolutional neural network
  • Deep learning
  • Image processing
  • Plant disease
  • Early pest
  • Disease identification
  • Precision agriculture
  • Phytopathology

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