Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning.

By Chad DeChant, Tyr Wiesner-Hanks, Siyuan Chen, Ethan L. Stewart, Jason Yosinski, Michael A. Gore, Rebecca J. Nelson, Hod Lipson. Phytopathology, 2017

353 citations recorded by the source corpus.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/854ad03af6458f68a969945612c688314088a35b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Plant Science

1. Johan Potgieter
2. Muhammad Hammad Saleem
3. Khalid Mahmood Arif
4. Sajid Hasan Apon
5. Yuehan Chen
6. Shujuan Zhang
7. Marcin Wozniak
8. F. Nowrin

Citations in the corpus

• Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks (2018, background): “[26] trained CNNs to automatically identify northern leaf blight of maize.”
• Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review (2020, methodology): “A study was conducted to generate a heat map of stressed lesion probabilities from small patches obtained by using a sliding window over a given image [45].”
• Plant Disease Detection and Classification by Deep Learning—A Review (2021, methodology): “[39] integrated multiple CNN classifiers to study high-resolution corn disease images.”
• Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, neutral): “It should be noted that 19 studies (S1, S6, S7, S11, S12, S13, S22, S35, S43, S61, S68, S70, S75, S84, S86, S91, S100, S116, S120) propose new architectures and 15 (S9, S18, S19, S23, S32, S34, S39, S41, S42, S49, S50, S51, S53, S65, S103) investigations customize classic algorithms to solve the pro”
• Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks (2019, background): “are not rare in crop disease detection currently [17]–[23].”
• Identification and recognition of rice diseases and pests using convolutional neural networks (2018, methodology): “Instead of resizing images to a smaller size and training a model end-to-end, [11] used a three stage architecture (consisting of multiple CNNs) and trained the stage-one model on full scaled images by dividing a single image into many smaller images.”
• An advanced deep learning models-based plant disease detection: A review of recent research (2023, methodology): “In a study conducted by (Dechant et al., 2017), a convolutional neural network (CNN) was trained to generate thermal maps of corn disease images, which were then used to classify the entire image as infected or noninfected.”
• Plant Disease Detection and Classification by Deep Learning (2019, methodology): “According to [70], different combinations of CNN were used and presented heat maps as input to the diseased plants’ images and provided the probability related to the occurrence of a particular type of disease.”
• A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases (2021, methodology): “Comparison of classification accuracy of machine learning and deep learning models [64].”