Impact of Color Spaces and Feature Sets in Automated Plant Diseases Classifier: A Comprehensive Review Based on Rice Plant Images

Verma, Toran; Dubey, Sipi

doi:10.1007/s11831-019-09364-6

Cited by 11 publications

(6 citation statements)

References 22 publications

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“…It can also monitor early-stage diseases and pests in complex scenes. Therefore, it has a better prospect to monitor and identify stressed crops by selecting characteristic spectral indices or VIs and building a correlation model between characteristic indices and DIs [118,119]. In conclusion, crop diseases and pests have the characteristics of long duration and strong reproductive and spreading abilities, posing a significant threat to crop growth and development.…”

Section: Discussion On Methods For Monitoring and Identifying Crop Di...mentioning

confidence: 99%

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Application Progress of UAV-LARS in Identification of Crop Diseases and Pests

Zhao,

Zhang,

Lan

et al. 2023

Agronomy

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Disease and pest stress is one of the important threats to crop growth and development, which have a very adverse impact on crop yield and quality every year, even leading to crop failure. Currently, the use of plant protection unmanned aerial vehicles (UAVs) for pesticide spraying is the most effective means of controlling crop diseases and pests. However, the areas where crop pests and diseases occur are often presented in “point-like” or “patchy” forms, and the UAV’s full-coverage spraying method results in a great waste of pesticides. Therefore, there is an urgent need to research a method for identifying the area of diseases and pest stress to achieve precise targeted spraying, in order to reduce the amount of pesticides used and improve their utilization. By analyzing the low-altitude remote sensing images of crop fields taken by UAVs, real-time pesticide spraying prescription maps can be generated to meet the demand for precise targeted spraying. This review focuses on the actual needs of precise targeted spraying by plant protection UAVs. Firstly, the RS monitoring mechanism of crop diseases and pests by UAVs is studied. Secondly, a comprehensive investigation of the literature on UAV Low-altitude Remote Sensing (UAV-LARS) technology for monitoring and identifying crop diseases and pests is conducted, summarizing the research progress in monitoring and identifying crop diseases and pests, especially in wheat, cotton, and rice. Finally, the key issues to be addressed and the future development direction of UAV-LARS monitoring of crop diseases and pests are proposed.

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Section: Discussion On Methods For Monitoring and Identifying Crop Di...mentioning

confidence: 99%

Section: Discussion On Methods For Monitoring and Identifying Crop Di...mentioning

confidence: 99%

Application Progress of UAV-LARS in Identification of Crop Diseases and Pests

Zhao,

Zhang,

Lan

et al. 2023

Agronomy

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“…For plant disease recognition, the color is the main manifestation of different plant diseases. Many studies have demonstrated that color transformation is effective to increase model robustness in plant disease recognition (El Sghair et al., 2017; Ghyar & Birajdar, 2017; Hlaing & Zaw, 2018; Pandian et al., 2019; Shrivastava et al., 2015; Verma & Dubey, 2020; Wagle et al., 2021).…”

Section: Application Of Few‐shot Learning For Datamentioning

confidence: 99%

Few‐shot learning for plant disease recognition: A review

Sun

Cao

et al. 2023

Agronomy Journal

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Monitoring plant diseases is essential for farmers to secure crop quantity and quality. Deep learning has recently been applied to plant disease recognition to help farmers take prompt and proper actions to prevent reductions in crop quantity and quality. Generally, deep learning requires a large‐scale dataset with supervised information annotated often by specialists. However, because collecting plant disease images in natural environments is difficult and obtaining proper annotations from specialists is costly, deep learning is infeasible for plant disease recognition tasks. Few‐shot learning (FSL) is an alternative for plant disease recognition using prior knowledge. Although FSL has attracted considerable attention, comprehensive reports on the application of FSL methods for plant disease recognition are required. Here, we introduce FSL with its applications in plant disease recognition. We begin with an overview of computer vision tasks using machine learning and FSL. We provide practical examples of FSL applications. Utilizing these practical examples, we describe different approaches for data augmentation and FSL methods of embedding, multitask learning, transfer learning, and meta‐learning. Further, we summarize how models are optimized for performance with reference to existing studies. Finally, the advantages and disadvantages are discussed, along with potential challenges for FSL applications in plant disease recognition.

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“…The existing studies mostly qualify the application effect of landscape colors [22][23][24][25][26][27][28][29], but rarely quantify the effect through image colorization. Besides, it is unreasonable to analyze landscape images with multiple standard colors with a single color space.…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction of Color Features from Landscape Images Based on Image Processing

Tan¹,

Yang²

2021

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The dominant color features determine the presentation effect and visual experience of landscapes. The existing studies rarely quantify the application effect of landscape colors through image colorization. Besides, it is unreasonable to analyze landscape images with multiple standard colors with a single color space. To solve the problem, this paper proposes an automatic extraction method for color features from landscape images based on image processing. Firstly, a landscape lighting model was constructed based on color constancy theories, and the quality of landscape images was improved with color constant image enhancement technology. In this way, the low-level color features were extracted from the landscape image library. Next, support vector machine (SVM) and fuzzy c-means (FCM) were innovatively integrated to extract high-level color features from landscape images. The proposed method was proved effective through experiments.

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Impact of Color Spaces and Feature Sets in Automated Plant Diseases Classifier: A Comprehensive Review Based on Rice Plant Images

Cited by 11 publications

References 22 publications

Application Progress of UAV-LARS in Identification of Crop Diseases and Pests

Application Progress of UAV-LARS in Identification of Crop Diseases and Pests

Few‐shot learning for plant disease recognition: A review

Automatic Extraction of Color Features from Landscape Images Based on Image Processing

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