Mobile Plant Disease Classifier, Trained with a Small Number of Images by the End User

Petrellis, Nikos; Antonopoulos, C.S.; Κεραμίδας, Γεώργιος; Voros, Nikolaos S.

doi:10.3390/agronomy12081732

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…The error in the landmark position estimation as presented in Tables 5, 7 and 9 and Figures 11 and 12 is largely due to the low contrast of the images in the employed dataset [34]. Other referenced approaches [23][24][25], are also tested with low-quality underwater images.…”

Section: Discussionmentioning

confidence: 99%

“…The photographs and videos of the developed dataset have been captured in two regions in Greece (Lampiri, Achaia and Gavathas, Lesvos island) and display Mediterranean fish species mostly seabream variations such as diplodous sargus annularis (white seabream), diplodus annularis seabream (spawn), oblada melanura (saddled seabream), pagrus pagrus (common seabream), etc. The dataset we developed is called Underwater Videos and Images with Mediterranean Fish Species in Shallow Waters (UVIMEF) and its first version is made publicly available in [34]. An example photograph and a video frame with the corresponding fish patches extracted, are shown in Figure 1.…”

Section: Dataset Tools and Target Environmentmentioning

confidence: 99%

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Fish Monitoring from Low-Contrast Underwater Images

et al. 2023

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A toolset supporting fish detection, orientation, tracking and especially morphological feature estimation with high speed and accuracy, is presented in this paper. It can be exploited in fish farms to automate everyday procedures including size measurement and optimal harvest time estimation, fish health assessment, quantification of feeding needs, etc. It can also be used in an open sea environment to monitor fish size, behavior and the population of various species. An efficient deep learning technique for fish detection is employed and adapted, while methods for fish tracking are also proposed. The fish orientation is classified in order to apply a shape alignment technique that is based on the Ensemble of Regression Trees machine learning method. Shape alignment allows the estimation of fish dimensions (length, height) and the localization of fish body parts of particular interest such as the eyes and gills. The proposed method can estimate the position of 18 landmarks with an accuracy of about 95% from low-contrast underwater images where the fish can be hardly distinguished from its background. Hardware and software acceleration techniques have been applied at the shape alignment process reducing the frame processing latency to less than 0.5 us on a general purpose computer and less than 16 ms on an embedded platform. As a case study, the developed system has been trained and tested with several Mediterranean fish species in the category of seabream. A large public dataset with low-resolution underwater videos and images has also been developed to test the proposed system under worst case conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Dataset Tools and Target Environmentmentioning

confidence: 99%

Fish Monitoring from Low-Contrast Underwater Images

et al. 2023

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Feature engineering to identify plant diseases using image processing and artificial intelligence: A comprehensive review

Javidan,

Banakar,

Rahnama

et al. 2024

Smart Agricultural Technology

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Automatic Estimation of Apple Orchard Blooming Levels Using the Improved YOLOv5

Chen

Wang

et al. 2022

Agronomy

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The estimation of orchard blooming levels and the determination of peak blooming dates are very important because they determine the timing of orchard flower thinning and are essential for apple yield and quality. In this paper, we propose an orchard blooming level estimation method for global-level and block-level blooming level estimation of orchards. The method consists of a deep learning-based apple flower detector, a blooming level estimator, and a peak blooming day finding estimator. The YOLOv5s model is used as the apple flower detector, which is improved by adding a coordinate attention layer and a small object detection layer and by replacing the model neck with a bidirectional feature pyramid network (BiFPN) structure to improve the performance of the apple flower detector at different growth stages. The robustness of the apple flower detector under different light conditions and the generalization across years was tested using apple flower data collected in 2021–2022. The trained apple flower detector achieved a mean average precision of 77.5%. The blooming level estimator estimated the orchard blooming level based on the proportion of flowers detected at different growth stages. Statistical results show that the blooming level estimator follows the trend of orchard blooming levels. The peak blooming day finding estimator successfully positioned the peak blooming time and provided information for the flower thinning timing decision. The method described in this paper is able to provide orchardists with accurate information on apple flower growth status and is highly automated.

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Mobile Plant Disease Classifier, Trained with a Small Number of Images by the End User

Cited by 4 publications

References 31 publications

Fish Monitoring from Low-Contrast Underwater Images

Fish Monitoring from Low-Contrast Underwater Images

Feature engineering to identify plant diseases using image processing and artificial intelligence: A comprehensive review

Automatic Estimation of Apple Orchard Blooming Levels Using the Improved YOLOv5

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