Using Deep Learning for Image-Based Plant Disease Detection

By Sharada Prasanna Mohanty, David P. Hughes, Marcel Salathé. 2016

4,343 citations recorded by the source corpus.

Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.

Subfields: Computer Vision and Pattern Recognition, Plant Science

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Citations in the corpus

Florence: A New Foundation Model for Computer Vision (2021, background): “The datasets in the benchmark include: CropDisease (Mohanty et al., 2016) (plant leaf images, 38 disease states over 14 plant species), EuroSAT (Helber et al., 2019) (RGB satellite images, 10 categories), ISIC 2018 (Codella et al., 2019; Tschandl et al., 2018) (dermoscopic images of skin lesions, 7…”
CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification (2021, methodology): “We validate this by performing transfer learning on 5 image classification tasks, including CIFAR10 [20], CIFAR100 [20], Pet [27], CropDisease [23], and ChestXRay8 [40].”
How Well Do Self-Supervised Models Transfer? (2020, background): “Thirdly: While core academic vision research is happy to focus on ImageNet as a benchmark, the wider community of computer vision practitioners work with diverse data types from medical [54] to agricultural [40], to earth-observation [24] data and beyond.”
Plants Disease Identification and Classification Through Leaf Images: A Survey (2018, methodology): “%) have used large size databases ranging in thousands [3, 19, 23, 31, 94, 100–102].”
Plant leaf disease classification using EfficientNet deep learning model (2021, methodology): “The results of studies (Chen et al., 2020b; Geetharamani and Pan-dian, 2019; Mohanty et al., 2016; Too et al., 2019) dealing with diseases of all plant species in PlantVillage dataset were compared with the re-sults of our study and shown in Table 9.”
Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture (2020, methodology): “Existing studies are, for example, investigating the estimation of crop yield using CNN and multispectral images together with climate data [197], plant disease detection using CNN and smartphone‐acquired images [198], crop classification using 3‐D CNN and multi‐temporal multispectral image”
Applications for deep learning in ecology (2018, background): “For example, CNNs already help detect plant diseases in olive trees 41 , cassavas ( Manihot esculenta ) 42 or various crops 43 .”
Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective (2018, neutral): “Moreover, noninvasive data acquisition enables us to continuously monitor phenotypic CNN-based approach Leaf CNN [78, 79] changes over time in plant life courses [75].”
An explainable deep machine vision framework for plant stress phenotyping (2018, methodology): “This capability for “transfer learning” (19) was investigated with several test images with IDC, Potassium deficiency, and SDS symptoms using nondestructive imaging protocols (e.”
Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, background): “The major challenge for agriculture is the correct identiﬁcation of the symptoms of major diseases that affect crops [2].”
Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks (2018, background): “Some researchers [27]–[29] can improve the identification accuracy of plant diseases to a certain extent by using different convolution neural network models and changing the ratio of training set size to testing set size.”
An In-field Automatic Wheat Disease Diagnosis System (2017, methodology): “Similarly, (Mohanty et al., 2016) trained a DCNN on a dataset of 54,306 images of diseased and healthy plant leaves to recognize 14 crop species and 26 diseases.”