Plant species classification using deep convolutional neural network

By M. Dyrmann, Henrik Karstoft, H. Midtiby. 2016

571 citations recorded by the source corpus.

How researchers see this paper

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

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.”
Plant Disease Detection and Classification by Deep Learning—A Review (2021, background): “1%/top-5% error [6], [20]–[22], precision and recall [10], [23], [24], F1 score [24], [25], training/validation accuracy and loss [25], [26], classification accuracy (CA) [27], [28], and mean average precision (mAP) are usually selected as the indicators of judgment.”
A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases (2021, methodology): “Besides, CNNs are successfully used in the field of agriculture for recognition of plant species [63], yield management [94,95], weed detection [96], soil and water management [97], fruit counting [98], diseases and pest detection [82,99], evaluating the nutrient status of plants [100], and monitori”
A Survey of Deep Learning Techniques for Weed Detection from Images (2021, methodology): “, 2018), 128×128 (Binguitcha-Fare & Sharma, 2019; Dyrmann et al., 2016; Espejo-Garcia et al., 2020), 224×224 (Binguitcha-Fare & Sharma, 2019; H.”