Automated Hyper-Parameter Tuning of a Mask R-CNN for Quantifying Common Rust Severity in Maize

Gerber, Mia; Pillay, Nelishia; Holan, Katerina; Whitham, Steven A.; Berger, David Kenneth

doi:10.1109/ijcnn52387.2021.9534417

Cited by 8 publications

(8 citation statements)

References 13 publications

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“…The accuracy of GLS_net is likely to increase as more images are added to the dataset. Hyper-parameter tuning is an additional approach that could be used to improve the GLS_net model [ 47 ].…”

Section: Discussionmentioning

confidence: 99%

“…The bottleneck in generating useful datasets is labelling each image to indicate either (i) the presence/absence of a disease symptom; or (ii) segmenting each image to define the positions of disease symptoms. Segmentation is important for applications where disease quantification is required, such as in crop breeding for disease resistance [ 28 , 47 ]. Current image datasets have the limitation that they are static, and not updated.…”

Section: Discussionmentioning

confidence: 99%

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Deep Learning Diagnostics of Gray Leaf Spot in Maize under Mixed Disease Field Conditions

Craze

Pillay

Joubert

et al. 2022

Plants

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Maize yields worldwide are limited by foliar diseases that could be fungal, oomycete, bacterial, or viral in origin. Correct disease identification is critical for farmers to apply the correct control measures, such as fungicide sprays. Deep learning has the potential for automated disease classification from images of leaf symptoms. We aimed to develop a classifier to identify gray leaf spot (GLS) disease of maize in field images where mixed diseases were present (18,656 images after augmentation). In this study, we compare deep learning models trained on mixed disease field images with and without background subtraction. Performance was compared with models trained on PlantVillage images with single diseases and uniform backgrounds. First, we developed a modified VGG16 network referred to as “GLS_net” to perform binary classification of GLS, which achieved a 73.4% accuracy. Second, we used MaskRCNN to dynamically segment leaves from backgrounds in combination with GLS_net to identify GLS, resulting in a 72.6% accuracy. Models trained on PlantVillage images were 94.1% accurate at GLS classification with the PlantVillage testing set but performed poorly with the field image dataset (55.1% accuracy). In contrast, the GLS_net model was 78% accurate on the PlantVillage testing set. We conclude that deep learning models trained with realistic mixed disease field data obtain superior degrees of generalizability and external validity when compared to models trained using idealized datasets.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Deep Learning Diagnostics of Gray Leaf Spot in Maize under Mixed Disease Field Conditions

Craze

Pillay

Joubert

et al. 2022

Plants

Self Cite

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“…At the same time as shown in [42] StarDist has still higher accuracy and requires less training data than MaskR-CNN for bubble detection. In addition, in contrast to Mask R-CNN, StarDist has only a few hyper-parameters that do not need careful tuning to achieve good results [43]; therefore, it was chosen to train the model StarDist for bubble instance segmentation as is discussed further in Section 4.…”

Section: Neural Network Methodology For Bubble Analysismentioning

confidence: 99%

Assessing the Mass Transfer Coefficient in Jet Bioreactors with Classical Computer Vision Methods and Neural Networks Algorithms

et al. 2023

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Development of energy-efficient and high-performance bioreactors requires progress in methods for assessing the key parameters of the biosynthesis process. With a wide variety of approaches and methods for determining the phase contact area in gas–liquid flows, the question of obtaining its accurate quantitative estimation remains open. Particularly challenging are the issues of getting information about the mass transfer coefficients instantly, as well as the development of predictive capabilities for the implementation of effective flow control in continuous fermentation both on the laboratory and industrial scales. Motivated by the opportunity to explore the possibility of applying classical and non-classical computer vision methods to the results of high-precision video records of bubble flows obtained during the experiment in the bioreactor vessel, we obtained a number of results presented in the paper. Characteristics of the bioreactor’s bubble flow were estimated first by classical computer vision (CCV) methods including an elliptic regression approach for single bubble boundaries selection and clustering, image transformation through a set of filters and developing an algorithm for separation of the overlapping bubbles. The application of the developed method for the entire video filming makes it possible to obtain parameter distributions and set dropout thresholds in order to obtain better estimates due to averaging. The developed CCV methodology was also tested and verified on a collected and labeled manual dataset. An onwards deep neural network (NN) approach was also applied, for instance the segmentation task, and has demonstrated certain advantages in terms of high segmentation resolution, while the classical one tends to be more speedy. Thus, in the current manuscript both advantages and disadvantages of the classical computer vision method (CCV) and neural network approach (NN) are discussed based on evaluation of bubbles’ number and their area defined. An approach to mass transfer coefficient estimation methodology in virtue of obtained results is also represented.

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“…Other groups using Mask R-CNN as component of their agriculture systems. Some concentrate on the common rust in maize and identify the severity of the disease by counting the number of pustules [63,64] . Besides images obtained from a ground-level camera, Stewart et al utilize a UAV at a 6-meter altitude and take over 7000 pictures [65] .…”

Section: Maize Disease Detectionmentioning

confidence: 99%

Deep learning approaches for object recognition in plant diseases: a review

Zhou,

Zhang,

et al. 2023

Intelligence & Robotics

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Plant diseases pose a significant threat to the economic viability of agriculture and the normal functioning of trees in forests. Accurate detection and identification of plant diseases are crucial for smart agricultural and forestry management. In recent years, the intersection of agriculture and artificial intelligence has become a popular research topic. Researchers have been experimenting with object recognition algorithms, specifically convolutional neural networks, to identify diseases in plant images. The goal is to reduce labor and improve detection efficiency. This article reviews the application of object detection methods for detecting common plant diseases, such as tomato, citrus, maize, and pine trees. It introduces various object detection models, ranging from basic to modern and sophisticated networks, and compares the innovative aspects and drawbacks of commonly used neural network models. Furthermore, the article discusses current challenges in plant disease detection and object detection methods and suggests promising directions for future work in learning-based plant disease detection systems.

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Automated Hyper-Parameter Tuning of a Mask R-CNN for Quantifying Common Rust Severity in Maize

Cited by 8 publications

References 13 publications

Deep Learning Diagnostics of Gray Leaf Spot in Maize under Mixed Disease Field Conditions

Deep Learning Diagnostics of Gray Leaf Spot in Maize under Mixed Disease Field Conditions

Assessing the Mass Transfer Coefficient in Jet Bioreactors with Classical Computer Vision Methods and Neural Networks Algorithms

Deep learning approaches for object recognition in plant diseases: a review

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