Plant disease identification from individual lesions and spots using deep learning

By J. G. Arnal Barbedo. Biosystems Engineering, 2019

627 citations recorded by the source corpus.

Deep learning is quickly becoming the standard technique for image classification. The main problem facing the automatic identification of plant diseases using this strategy is the lack of image databases capable of representing the wide variety of conditions and symptom characteristics found in practice. Data augmentation techniques decrease the impact of this problem, but those cannot reproduce most of the practical diversity. This paper explores the use of individual lesions and spots for the task, rather than considering the entire leaf. Since each region has its own characteristics, the variability of the data is increased without the need for additional images. This also allows the identification of multiple diseases affecting the same leaf. On the other hand, suitable symptom segmentation still needs to be done manually, preventing full automation. The accuracies obtained using this approach were, in average, 12% higher than those achieved using the original images. Additionally, no crop had accuracies below 75%, even when as many as 10 diseases were considered. Although the database does not cover the entire range of practical possibilities, these results indicate that, as long as enough data is available, deep learning techniques are effective for plant disease detection and recognition.

Subfields: Plant Science

View Paper: https://www.semanticscholar.org/paper/c79742d195fd9ca232ee73c67cc451eb8305b9b5

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Citations in the corpus

Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification (2021, background): “00% (improved 12%) Arnal Barbedo (2019) [86] From 5000 to 43,398 images Resizing, crop, rotation, noise.”
An advanced deep learning models-based plant disease detection: A review of recent research (2023, background): “Table 3 offers a full comparison of the several sub-methods that make up the categorized network system, showing the benefits and drawbacks of each option (Mohanty et al., 2016; Brahimi et al., 2018; Garcia and Barbedo, 2019).”
A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases (2021, methodology): “The researcher in [65] applied the GoogLeNet model on the Expanded dataset (XDB).”
Identification and recognition of rice diseases and pests using convolutional neural networks (2018, methodology): “Images of individual lesions and spots instead of image of whole leaf were used by [6] for identifying 79 diseases of 14 plant species.”
Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress (2019, methodology): “In addition to our images, 575 images made available by Barbedo (2019) were added to our dataset, accounting for 2722 symptom images.”
Plant Disease Detection and Classification by Deep Learning (2019, methodology): “In [76], an approach based on the individual symptoms/spots of diseases in the plants was introduced by using a DL model for detecting plant diseases.”
Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, neutral): “S16 [60] 2019 4 GoogLeNet 94% PDDB Embrapa 46.”
Plant Disease Detection and Classification by Deep Learning—A Review (2021, methodology): “The results showed that the method can also be applied to other crops and plants, including tomato, tobacco, banana, and etc. Barbedo [42] explored the use of individual lesions and spots for the task, rather than considering the entire leaf, and by using the DL models to identify the plant diseases”
Grape detection, segmentation and tracking using deep neural networks and three-dimensional association (2019, background): “Fruit detection can also be a preliminary step for disease and nutrient deficiency monitoring [3] and a crucial component on actuation, for example, automated spraying and harvesting may be an important application considering the declining in agricultural labor force [44].”