Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case

By Alexander Johannes, Artzai Picon, Aitor Alvarez-Gila, Jone Echazarra, S. Rodríguez-Vaamonde, Ana Díez Navajas, Amaia Ortiz-Barredo. Computers and Electronics in Agriculture, 2017

360 citations recorded by the source corpus.

Automatic Plant disease diagnosis for early disease symptoms.Novel image processing algorithm in combination with machine learning inference methods.A performance testing on wheat was done with 7 mobile devices over 3 seasons.Obtained results reveal AuC metrics higher than 0.80 for all the analyzed diseases. Disease diagnosis based on the detection of early symptoms is a usual threshold taken into account for integrated pest management strategies. Early phytosanitary treatment minimizes yield losses and increases the efficacy and efficiency of the treatments. However, the appearance of new diseases associated to new resistant crop variants complicates their early identification delaying the application of the appropriate corrective actions. The use of image based automated identification systems can leverage early detection of diseases among farmers and technicians but they perform poorly under real field conditions using mobile devices. A novel image processing algorithm based on candidate hot-spot detection in combination with statistical inference methods is proposed to tackle disease identification in wild conditions. This work analyses the performance of early identification of three European endemic wheat diseases septoria, rust and tan spot. The analysis was done using 7 mobile devices and more than 3500 images captured in two pilot sites in Spain and Germany during 2014, 2015 and 2016. Obtained results reveal AuC (Area under the Receiver Operating CharacteristicROCCurve) metrics higher than 0.80 for all the analyzed diseases on the pilot tests under real conditions.

Subfields: Analytical Chemistry, Plant Science

View Paper: https://www.semanticscholar.org/paper/91381bedaa6d880a393122be0064096e5d5e432d

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In Analytical Chemistry

In Plant Science

Citations in the corpus

Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress (2019, background): “Johannes et al. (2017) developed a system capable of identifying diseases in photos of wheat leaves obtained by smartphones.”
A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition (2017, methodology): “In [45], an image processing and statistical inference approach was introduced to identify three types of leaf diseases in wheat.”
Plant leaf disease classification using EfficientNet deep learning model (2021, methodology): “They deployed their proposed method on smartphones and evaluated them in a real field environment (Johannes et al., 2017).”
Plant Disease Detection and Classification by Deep Learning (2019, methodology): “An algorithm based on the hot spot technique was also used in [80], in which those spots were extracted by modiﬁcation in the segmented image to attain color constancy.”
High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture (2019, background): “With a focus on small-scale farmers in developing countries, an open access mobile application based on deep machine learning to detect foliar symptoms of diseases in cassava using machine learning has been developed and tested in Tanzania [86,87].”