A survey of image-based computational learning techniques for frost detection in plants

Shammi, Sayma; Sohel, Ferdous; Diepeveen, Dean; Zander, Sebastian; Jones, M. G. K.

doi:10.1016/j.inpa.2022.02.003

Cited by 10 publications

(8 citation statements)

References 117 publications

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“…Additionally, it is crucial to promptly evaluate the level of frost tolerance of conifers during specific time periods. Currently, multispectral and hyperspectral data are used to determine the degree of damage to plants caused by negative temperatures [83,84] but not their frost tolerance-their ability to tolerate certain negative temperatures. The ability to solve this issue depends on the correlation between negative temperatures and the values of PRI and CCI.…”

Section: Discussionmentioning

confidence: 99%

Vegetation and Dormancy States Identification in Coniferous Plants Based on Hyperspectral Imaging Data

Dmitriev,

Kozlovsky,

Dmitrieva

2024

Horticulturae

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Conifers are a common type of plant used in ornamental horticulture. The prompt diagnosis of the phenological state of coniferous plants using remote sensing is crucial for forecasting the consequences of extreme weather events. This is the first study to identify the “Vegetation” and “Dormancy” states in coniferous plants by analyzing their annual time series of spectral characteristics. The study analyzed Platycladus orientalis, Thuja occidentalis and T. plicata using time series values of 81 vegetation indices and 125 spectral bands. Linear discriminant analysis (LDA) was used to identify “Vegetation” and “Dormancy” states. The model contained three to four independent variables and achieved a high level of correctness (92.3 to 96.1%) and test accuracy (92.1 to 96.0%). The LDA model assigns the highest weight to vegetation indices that are sensitive to photosynthetic pigments, such as the photochemical reflectance index (PRI), normalized PRI (PRI_norm), the ratio of PRI to coloration index 2 (PRI/CI2), and derivative index 2 (D2). The random forest method also diagnoses the “Vegetation” and “Dormancy” states with high accuracy (97.3%). The vegetation indices chlorophyll/carotenoid index (CCI), PRI, PRI_norm and PRI/CI2 contribute the most to the mean decrease accuracy and mean decrease Gini. Diagnosing the phenological state of conifers throughout the annual cycle will allow for the effective planning of management measures in conifer plantations.

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Section: Discussionmentioning

confidence: 99%

Vegetation and Dormancy States Identification in Coniferous Plants Based on Hyperspectral Imaging Data

Dmitriev,

Kozlovsky,

Dmitrieva

2024

Horticulturae

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“…However, plants and crops can suffer from various factors, e.g. chemical (Pimentel 2009), frost (Shammi et al 2022), weeds (Hasan et al 2021), and insects (Liu and Wang 2021). Insects are a major problem in the agricultural sector, and are one of the main biotic factors which cause agricultural losses.…”

Section: Introductionmentioning

confidence: 99%

“…However, plants and crops can suffer from various factors, e.g. chemical (Pimentel 2009), frost (Shammi et al . 2022), weeds (Hasan et al .…”

Section: Introductionmentioning

confidence: 99%

Insect detection from imagery using YOLOv3-based adaptive feature fusion convolution network

Amrani

Sohel

Diepeveen

et al. 2022

Crop & Pasture Science

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Context Insects are a major threat to crop production. They can infect, damage, and reduce agricultural yields. Accurate and fast detection of insects will help insect control. From a computer algorithm point of view, insect detection from imagery is a tiny object detection problem. Handling detection of tiny objects in large datasets is challenging due to small resolution of the insects in an image, and other nuisances such as occlusion, noise, and lack of features. Aims Our aim was to achieve a high-performance agricultural insect detector using an enhanced artificial intelligence machine learning technique. Methods We used a YOLOv3 network-based framework, which is a high performing and computationally fast object detector. We further improved the original feature pyramidal network of YOLOv3 by integrating an adaptive feature fusion module. For training the network, we first applied data augmentation techniques to regularise the dataset. Then, we trained the network using the adaptive features and optimised the hyper-parameters. Finally, we tested the proposed network on a subset dataset of the multi-class insect pest dataset Pest24, which contains 25 878 images. Key results We achieved an accuracy of 72.10%, which is superior to existing techniques, while achieving a fast detection rate of 63.8 images per second. Conclusions We compared the results with several object detection models regarding detection accuracy and processing speed. The proposed method achieved superior performance both in terms of accuracy and computational speed. Implications The proposed method demonstrates that machine learning networks can provide a foundation for developing real-time systems that can help better pest control to reduce crop damage.

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“…5 To avoid or even minimize these adverse effects, early frost detection should be employed accurately along with appropriate supporting technologies. 6 In principle, there are several types of frost. 7 (I) Advection frost which has a form of ice spikes and forms when the target solid surface is subjected to a cold wind.…”

Section: Introductionmentioning

confidence: 99%

“… 5 To avoid or even minimize these adverse effects, early frost detection should be employed accurately along with appropriate supporting technologies. 6 …”

Section: Introductionmentioning

confidence: 99%

Frost formation through super-cooled water within micron gap of galvanic coupled arrays

et al. 2022

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A survey of image-based computational learning techniques for frost detection in plants

Cited by 10 publications

References 117 publications

Vegetation and Dormancy States Identification in Coniferous Plants Based on Hyperspectral Imaging Data

Vegetation and Dormancy States Identification in Coniferous Plants Based on Hyperspectral Imaging Data

Insect detection from imagery using YOLOv3-based adaptive feature fusion convolution network

Frost formation through super-cooled water within micron gap of galvanic coupled arrays

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