Convolutional Neural Networks for the Automatic Identification of Plant Diseases

By Justine Boulent, Samuel Foucher, Jérôme Théau, Pierre-Luc St-Charles. Frontiers in Plant Science, 2019

347 citations recorded by the source corpus.

Deep learning techniques, and in particular Convolutional Neural Networks (CNNs), have led to significant progress in image processing. Since 2016, many applications for the automatic identification of crop diseases have been developed. These applications could serve as a basis for the development of expertise assistance or automatic screening tools. Such tools could contribute to more sustainable agricultural practices and greater food production security. To assess the potential of these networks for such applications, we survey 19 studies that relied on CNNs to automatically identify crop diseases. We describe their profiles, their main implementation aspects and their performance. Our survey allows us to identify the major issues and shortcomings of works in this research area. We also provide guidelines to improve the use of CNNs in operational contexts as well as some directions for future research.

Subfields: Plant Science

View Paper: https://www.semanticscholar.org/paper/33267246b3a4ae75c716714d4fa6ac15b324e5fb

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H. Aljahdali
Santos Gracia Villar
Seifedien Kadry
Abhineet Anand
Vinay Gautam
N. Trivedi
Divya Anand
N. Goyal

Citations in the corpus

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases (2021, neutral): “In AlexNet, images are resized to 227 × 227 [34].”
Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network (2021, background): “The authors looked at 19 studies that employed CNNs to identify plant diseases and assess their overall utility [49].”