Smartphone assist deep neural network to detect the citrus diseases in Agri-informatics

Barman, Utpal; Choudhury, Ridip Dev

doi:10.1016/j.gltp.2021.10.004

Cited by 14 publications

(10 citation statements)

References 13 publications

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“…Artificial neural network (ANN) based regression models can be used to predict output variables as a function of the input variable. Still, ANNs have large sample size requirements with their performance directly related to the adequacy of their training data [113]. Therefore, regression algorithms for interpreting SbS results should be trained on large quantities of data acquired under diverse conditions (e.g., different lighting conditions, angles, times of day, recorded by different users, etc.)…”

Section: Ai In Results Interpretation (Regression)mentioning

confidence: 99%

Best practices and current implementation of emerging smartphone-based (bio)sensors – Part 1: Data handling and ethics

Ross

Zhao

Bosman

et al. 2023

TrAC Trends in Analytical Chemistry

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Section: Ai In Results Interpretation (Regression)mentioning

confidence: 99%

Best practices and current implementation of emerging smartphone-based (bio)sensors – Part 1: Data handling and ethics

Ross

Zhao

Bosman

et al. 2023

TrAC Trends in Analytical Chemistry

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“…Dentro de la huerta existen otro tipo de árboles frutales (menos de 10 árboles) como mandarinos, limeros, guayabos y duraznos además de maleza, por lo que se generó la clase otra vegetación. Del total de los 145 polígonos que se generaron para la clasificación, el 70% se utilizó para entrenar los algoritmos y el 30% restante se utilizó para validar los resultados (Barman & Choudhury, 2022). Los polígonos que se utilizaron para el entrenamiento y validación fueron seleccionados de manera aleatoria.…”

Section: Generación De Modelosunclassified

“…La identificación y manejo de los árboles de naranja enfermos es crucial para mitigar el impacto de estas enfermedades en los huertos (Moriya et al, 2021). Los métodos tradicionales de detección de enfermedades se basan en la inspección visual realizada por seres humanos, lo cual puede ser tedioso y está sujeto a errores (Barman & Choudhury, 2022). Por otro lado, las técnicas de percepción remota, en combinación con algoritmos de aprendizaje automático, han demostrado un gran potencial para automatizar y mejorar los procesos de detección de enfermedades (Moussaid et al, 2020).…”

Section: Introductionunclassified

Identification of the phytosanitary status of trees using machine learning and very high spatial resolution images

Díaz Rivera,

Aguirre-Salado,

Loredo-Osti

et al. 2024

Sci. agropecu.

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Tree diseases contribute to significant economic and food losses in the agricultural sector. Early detection of phytosanitary problems in trees with non-destructive methods is essential to guarantee sustainable orange production. This study presents the findings of a designed methodology conducted to identify diseased orange trees in an orchard situated in the citrus belt of Mexico, specifically in the Rioverde region of San Luis Potosi. To accomplish this, we captured images using a multispectral camera with very high spatial resolution, which was mounted on an unmanned aerial vehicle. These images were used to construct a georeferenced orthomosaic of the orchard. Six thematic classes were established to distinguish various health levels among the trees. We employed several supervised classification algorithms at the pixel level, including Random Forest (RF), K-Nearest Neighbor (KNN), Spectral Angle Mapper (SAM), Support Vector Machine (SVM), and Maximum Likelihood (ML). Considering the classification accuracy achieved by each algorithm, they can be ranked as follows: Maximum Likelihood (ML) with 88.10%, Support Vector Machine (SVM) with 77.38%, Spectral Angle Mapper (SAM) with 76.19%, K-Nearest Neighbor (KNN) with 64.68%, and Random Forest (RF) with 61.90%. These results successfully identified the phytosanitary status of all the trees in the orchard with an acceptable level of accuracy, providing valuable management information for the grower.

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“…Barman and Choudhury [ 25 ] designed a smartphone app to detect diseases on citrus leaves. They relied on CNNs to classify the samples based on the collected images.…”

Section: Theoretical References and Related Workmentioning

confidence: 99%

Using Mobile Edge AI to Detect and Map Diseases in Citrus Orchards

Silva

Luz

et al. 2023

Sensors

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Deep Learning models have presented promising results when applied to Agriculture 4.0. Among other applications, these models can be used in disease detection and fruit counting. Deep Learning models usually have many layers in the architecture and millions of parameters. This aspect hinders the use of Deep Learning on mobile devices as they require a large amount of processing power for inference. In addition, the lack of high-quality Internet connectivity in the field impedes the usage of cloud computing, pushing the processing towards edge devices. This work describes the proposal of an edge AI application to detect and map diseases in citrus orchards. The proposed system has low computational demand, enabling the use of low-footprint models for both detection and classification tasks. We initially compared AI algorithms to detect fruits on trees. Specifically, we analyzed and compared YOLO and Faster R-CNN. Then, we studied lean AI models to perform the classification task. In this context, we tested and compared the performance of MobileNetV2, EfficientNetV2-B0, and NASNet-Mobile. In the detection task, YOLO and Faster R-CNN had similar AI performance metrics, but YOLO was significantly faster. In the image classification task, MobileNetMobileV2 and EfficientNetV2-B0 obtained an accuracy of 100%, while NASNet-Mobile had a 98% performance. As for the timing performance, MobileNetV2 and EfficientNetV2-B0 were the best candidates, while NASNet-Mobile was significantly worse. Furthermore, MobileNetV2 had a 10% better performance than EfficientNetV2-B0. Finally, we provide a method to evaluate the results from these algorithms towards describing the disease spread using statistical parametric models and a genetic algorithm to perform the parameters’ regression. With these results, we validated the proposed pipeline, enabling the usage of adequate AI models to develop a mobile edge AI solution.

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Smartphone assist deep neural network to detect the citrus diseases in Agri-informatics

Cited by 14 publications

References 13 publications

Best practices and current implementation of emerging smartphone-based (bio)sensors – Part 1: Data handling and ethics

Best practices and current implementation of emerging smartphone-based (bio)sensors – Part 1: Data handling and ethics

Identification of the phytosanitary status of trees using machine learning and very high spatial resolution images

Using Mobile Edge AI to Detect and Map Diseases in Citrus Orchards

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