A. Picon, M. Seitz, A. Alvarez-Gila, P. Mohnke, A. Ortiz-Barredo, and J. Echazarra, “Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions,” Computers and Electronics in Agriculture, p. 105093, Nov. 2019.
Abstract:
Convolutional Neural Networks (CNN) have demonstrated their capabilities on the agronomical field, especially for plant visual symptoms assessment. As these models grow both in the number of training images and in the number of supported crops and diseases, there exist the dichotomy of (1) generating smaller models for specific crop or, (2) to generate a unique multi-crop model in a much more complex task (especially at early disease stages) but with the benefit of the entire multiple crop image dataset variability to enrich image feature description learning.
In this work we first introduce a challenging dataset of more than one hundred-thousand images taken by cell phone in real field wild conditions. This dataset contains almost equally distributed disease stages of seventeen diseases and five crops (wheat, barley, corn, rice and rape-seed) where several diseases can be present on the same picture.
When applying existing state of the art deep neural network methods to validate the two hypothesised approaches, we obtained a balanced accuracy (BAC=0.92) when generating the smaller crop specific models and a balanced accuracy (BAC=0.93) when generating a single multi-crop model.
In this work, we propose three different CNN architectures that incorporate contextual non-image meta-data such as crop information onto an image based Convolutional Neural Network. This combines the advantages of simultaneously learning from the entire multi-crop dataset while reducing the complexity of the disease classification tasks. The crop-conditional plant disease classification network that incorporates the contextual information by concatenation at the embedding vector level obtains a balanced accuracy of 0.98 improving all previous methods and removing 71% of the miss-classifications of the former methods.
Bibtex:
@article{piconCropConditionalConvolutional2019a,
title = {Crop Conditional Convolutional Neural Networks for Massive Multi-Crop Plant Disease Classification over Cell Phone Acquired Images Taken on Real Field Conditions},
issn = {0168-1699},
abstract = {Convolutional Neural Networks (CNN) have demonstrated their capabilities on the agronomical field, especially for plant visual symptoms assessment. As these models grow both in the number of training images and in the number of supported crops and diseases, there exist the dichotomy of (1) generating smaller models for specific crop or, (2) to generate a unique multi-crop model in a much more complex task (especially at early disease stages) but with the benefit of the entire multiple crop image dataset variability to enrich image feature description learning. In this work we first introduce a challenging dataset of more than one hundred-thousand images taken by cell phone in real field wild conditions. This dataset contains almost equally distributed disease stages of seventeen diseases and five crops (wheat, barley, corn, rice and rape-seed) where several diseases can be present on the same picture. When applying existing state of the art deep neural network methods to validate the two hypothesised approaches, we obtained a balanced accuracy (BAC=0.92) when generating the smaller crop specific models and a balanced accuracy (BAC=0.93) when generating a single multi-crop model. In this work, we propose three different CNN architectures that incorporate contextual non-image meta-data such as crop information onto an image based Convolutional Neural Network. This combines the advantages of simultaneously learning from the entire multi-crop dataset while reducing the complexity of the disease classification tasks. The crop-conditional plant disease classification network that incorporates the contextual information by concatenation at the embedding vector level obtains a balanced accuracy of 0.98 improving all previous methods and removing 71\% of the miss-classifications of the former methods.},
language = {en},
journal = {Computers and Electronics in Agriculture},
doi = {10.1016/j.compag.2019.105093},
author = {Picon, Artzai and Seitz, Maximiliam and {Alvarez-Gila}, Aitor and Mohnke, Patrick and {Ortiz-Barredo}, Amaia and Echazarra, Jone},
month = nov,
year = {2019},
keywords = {Deep learning,_read,Image processing,Convolutional neural network,Precision agriculture,Disease identification,Early pest,Plant disease,Contextual meta-data,Contextual meta-data conditional neural network,Crop protection,Multi-crop classification,Multi-label classification,Phyto-pathology},
pages = {105093}
}