Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild

By Artzai Picon, Aitor Alvarez-Gila, Maximiliam Seitz, Amaia Ortiz-Barredo, Jone Echazarra, Alexander Johannes. Computers and Electronics in Agriculture, 2019

486 citations recorded by the source corpus.

Abstract Fungal infection represents up to 50 % of yield losses, making it necessary to apply effective and cost efficient fungicide treatments, whose efficacy depends on infestation type, situation and time. In these cases, a correct and early identification of the specific infection is mandatory to minimize yield losses and increase the efficacy and efficiency of the treatments. Over the last years, a number of image analysis-based methodologies have been proposed for automatic image disease identification. Among these methods, the use of Deep Convolutional Neural Networks (CNNs) has proven tremendously successful for different visual classification tasks. In this work we extend previous work by Johannes et al. (2017) with an adapted Deep Residual Neural Network-based algorithm to deal with the detection of multiple plant diseases in real acquisition conditions where different adaptions for early disease detection have been proposed. This work analyses the performance of early identification of three relevant European endemic wheat diseases: Septoria ( Septoria triciti ), Tan Spot ( Drechslera triciti-repentis ) and Rust ( Puccinia striiformis & Puccinia recondita ). The analysis was done using different mobile devices, and more than 8178 images were captured in two pilot sites in Spain and Germany during 2014 , 2015 and 2016. Obtained results reveal an overall improvement of the balanced accuracy from 0.78 (Johannes et al., 2017) up to 0.87 under exhaustive testing, and balanced accuracies greater than 0.96 on a pilot test performed in Germany.

Subfields: Cell Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/5b1fa8135d6a27bb512019f5e279475b1b04b72b

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Citations in the corpus

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases (2021, methodology): “Wheat Septoria, Tan Spot and Rust ResNet50 [68] Author-defined dataset of 8178 images Accuracy: 96.”
Plant Disease Detection and Classification by Deep Learning (2019, background): “87) [84] DCNN Real environment Wheat Accuracy (85%) [114] ResNet-50 Real environment Wheat Balanced Accuracy (87%) [79] GPDCNN Real environment Cucumber CA (94.”
Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, unknown): “The customized Data Sets are present in 66 studies (S1, S2, S3, S6, S7, S8, S9, S12, S15, S17, S18, S20, S22, S24, S25, S28, S32, S33, S34, S36 , S37, S38, S40, S43, S44, S45, S46, S47, S49, S50, S52, S58, S64, S68, S69, S70, S71, S73, S74, S75, S77, S79, S80, S82, S84 , S87, S88, S89, S90, S91, S92”
Research and development in agricultural robotics: a perspective of digital farming. (2018, background): “[203] Picon A, Alvarez-Gila A, Seitz M, Ortiz-Barredo A, Echazarra J, Johannes A.”
Plant Disease Detection and Classification by Deep Learning—A Review (2021, methodology): “[45] in order to extract the detailed features of the wheat disease symptoms, the first 7× 7 convolutional layer of the ResNet50 network was replaced with two 3 × 3 convolutions and the sigmoid activation function was used”