Deep learning for plant identification using vein morphological patterns

By Guillermo Luis Grinblat, Lucas C. Uzal, Mónica G. Larese, Pablo M. Granitto. Computers and Electronics in Agriculture, 2016

559 citations recorded by the source corpus.

Display Omitted Deep convolutional neural network (CNN) for plant identification focusing on leaf vein patterns.No task-specific feature extractors needed.Improved the state of the art accuracy on a legume species recognition task.Visualization of relevant vein patterns. We propose using a deep convolutional neural network (CNN) for the problem of plant identification from leaf vein patterns. In particular, we consider classifying three different legume species: white bean, red bean and soybean. The introduction of a CNN avoids the use of handcrafted feature extractors as it is standard in state of the art pipeline. Furthermore, this deep learning approach significantly improves the accuracy of the referred pipeline. We also show that the reported accuracy is reached by increasing the model depth. Finally, by analyzing the resulting models with a simple visualization technique, we are able to unveil relevant vein patterns.

Subfields: Analytical Chemistry, Plant Science

View Paper: https://www.semanticscholar.org/paper/d04cf73d35b94f8404d4086b41a85fbe70c8cf0f

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In Analytical Chemistry

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Citations in the corpus

How deep learning extracts and learns leaf features for plant classification (2017, methodology): “findings demonstrate that CNN trained with leaf patches is capable of recognising the relevant vein patterns and differentiating them among species without needing any manual segmentation or pre-processing [30] on veins.”
Plant Disease Detection and Classification by Deep Learning (2019, methodology): “[48,50] employed user-modified CNN while [49] used AlexNet architecture.”
Deep learning in agriculture: A survey (2018, methodology): “CNN had also superior performance than Penalized Discriminant Analysis (Grinblat, Uzal, Larese, & Granitto, 2016), SVM Regression (Kuwata & Shibasaki, 2015), area-based techniques (Rahnemoonfar & Sheppard, 2017), texturebased regression models (Chen, et al., 2017), LMC classifiers (Xinshao & Cheng, ”
Deep convolution neural network for image recognition (2018, methodology): “In the same area, Grinbat and his colleagues (Grinblat et al., 2016) use Deep convolutional neural networks (DCNN) for plant identification by classifying three different legume species: white beans, red beans, and soybeans.”
An In-field Automatic Wheat Disease Diagnosis System (2017, methodology): “(Dyrmann et al., 2016; Grinblat et al., 2016) designed their task-speciﬁc DCNN architectures to perform plant species classiﬁcation.”
A review of the use of convolutional neural networks in agriculture (2018, background): “Moreover, CNN achieved 6% higher CA than random forests (Kussul et al ., 2017), 2% better CA than Penalized Discriminant Analysis (Grinblat et al ., 2016), 41% improved CA when compared with ANN (Lee et al ., 2015) and 24% lower RMSE compared with Support Vector Regression (Kuwata and Shibasaki, 201”
PlantDoc: A Dataset for Visual Plant Disease Detection (2019, background): “Recent work [8] has looked into neural networks for the identification of three different legume species based on the morphological patterns of leaves veins.”
Machine Learning in Agriculture: A Review (2018, methodology): “A method for the identiﬁcation and classiﬁcation of three legume species, namely, white beans, red beans, and soybean, via leaf vein patterns has been presented in [97].”