Factors influencing the use of deep learning for plant disease recognition

By J. G. Arnal Barbedo. Biosystems Engineering, 2018

543 citations recorded by the source corpus.

Deep learning is quickly becoming one of the most important tools for image classification. This technology is now beginning to be applied to the tasks of plant disease classification and recognition. The positive results that are being obtained using this approach hide some issues that are seldom taken into account in the respective experiments. This article presents an investigation into the main factors that affect the design and effectiveness of deep neural nets applied to plant pathology. An in-depth analysis of the subject, in which advantages and shortcomings are highlighted, should lead to more realistic conclusions on the subject. The arguments used throughout the text are built upon both studies found in the literature and experiments carried out using an image database carefully built to reflect and reproduce many of the conditions expected to be found in practice. This database, which contains almost 50,000 images, is being made freely available for academic purposes.

Subfields: Analytical Chemistry, Plant Science

View Paper: https://www.semanticscholar.org/paper/cfd9fadfeffd7b3dc71cfeed3a17d8df11ef9985

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In Analytical Chemistry

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Citations in the corpus

An advanced deep learning models-based plant disease detection: A review of recent research (2023, background): “Furthermore, variations in the presence of illness and the growth of a pest can lead to subtle differences in the characterization of the same diseases and pests in different regions, resulting in “intra-class distinctions” (Barbedo, 2018).”
Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress (2019, background): “Although there are initiatives that are using the concept of social networks to accelerate this process, this practice can lead to unreliable data (Barbedo, 2018a).”
Plant Disease Detection and Classification by Deep Learning—A Review (2021, background): “Recently, the convolutional neural networks (CNN), a special of deep learning techniques, are quickly becoming the preferred methods [7].”
Tomato Diseases and Pests Detection Based on Improved Yolo V3 Convolutional Neural Network (2020, background): “However, the automatic identi ﬁ cation and detection of plant diseases and insect pests are mainly faced with the following dif ﬁ culties (Barbedo, 2016a; Barbedo, 2016b; Barbedo, 2017; Barbedo, 2018a; Barbedo, 2018b). i.”