A Comparative Survey of Maize Leaf Diseases using Pre-Trained Convolutional Neural Networks

Kanchanadevi, K.; Sandhia, G. K.

doi:10.1109/icaect57570.2023.10117735

Cited by 1 publication

(2 citation statements)

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“…Next was the pre-trained ResNet152V2 model (He et al, 2016), which is the most complex and recent model architectures available of their respective model series. The ResNet152V2 model was selected due to its prevalence in image classification literature in a variety of different fields from disease ratings in agriculture (Kanchanadevi & Sandhia, 2023;Nigam et al, 2023) to medical research (Sulaiman et al, 2023). The final model was the pre-trained EfficientNetV2L model (Tan & Le, 2021), which was selected due to its recent use in plant disease detection (Shovon et al, 2023;Ulutaş & Aslantaş, 2023).…”

Section: Deep Learningmentioning

confidence: 99%

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Phenotyping and predicting wheat spike characteristics using image analysis and machine learning

Hammers,

Winn,

Ben‐Hur

et al. 2023

The Plant Phenome Journal

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Improvements in trait phenotyping are needed to increase the quantity and quality of data available for genetic improvement of crops. In this study, we used moderate throughput image analysis and machine learning as a pipeline for phenotyping a key wheat spike characteristic: spikelet number per spike. A population of 594 soft red winter wheat inbred lines was evaluated in the field for 2 years and images of wheat spikes were taken and used to train deep‐learning algorithms to predict spikelet number. A total of 12,717 images were used to train, test, and validate a basic regression convolutional neural network (CNN), a visual geometry group application regression model, VGG16, the ResNet152V2 model, and the EfficientNetV2L model. The EfficientNetV2L model was the most accurate, having the lowest mean absolute error, second lowest root mean square error, and highest coefficient of determination (mean absolute error [MAE] = 0.60, root mean square error [RMSE] = 0.79, and R2 = 0.90). The ResNet152V2 model was slightly less accurate with a slightly better fit (MAE = 0.61,m RMSE = 0.78, and R2 = 0.87), followed by the basic CNN (MAE = 0.75, RMSE = 1.00, and R2 = 0.74) and finally by the VGG16 (MAE = 1.51, RMSE = 1.29, and R2 = 0.076). With an average error of just above one half of a spikelet, utilizing image analysis and machine learning counting methods could be used for multiple breeding applications, including direct selection of spikelet number, to provide data to identify quantitative trait loci, or for training whole genome selection models.

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Section: Deep Learningmentioning

confidence: 99%

“…Some well‐known architectures include VGG16, ResNet models, and EfficientNet models. Many studies utilize these complex models in lieu of generating a new model from scratch (Kanchanadevi & Sandhia, 2023; Khaki et al., 2020; Nigam et al., 2023; Rao et al., 2022).…”

Section: Introductionmentioning

confidence: 99%