Precision Agriculture Technologies for Management of Plant Diseases

Balasundram, Siva K.; Golhani, Kamlesh; Shamshiri, Redmond R.; Vadamalai, Ganesan

doi:10.1007/978-3-030-35955-3_13

Cited by 20 publications

(10 citation statements)

References 67 publications

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“…The ground multispectral data used in this study were collected by a multispectral camera (RedEdge-MX, Micasense, Seattle, WA, USA), which has been widely used in the field of agricultural remote sensing [29]. The spectral parameters of the multispectral sensor are shown in Table 1.…”

Section: Data Acquisition 221 Spectral Datamentioning

confidence: 99%

Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

Chen

Zhao

et al. 2021

Remote Sensing

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The production of high-quality tea by Camellia sinensis (L.) O. Ktze is the goal pursued by both producers and consumers. Rapid, nondestructive, and low-cost monitoring methods for monitoring tea quality could improve the tea quality and the economic benefits associated with tea. This research explored the possibility of monitoring tea leaf quality from multi-spectral images. Threshold segmentation and manual sampling methods were used to eliminate the image background, after which the spectral features were constructed. Based on this, the texture features of the multi-spectral images of the tea canopy were extracted. Three machine learning methods, partial least squares regression, support vector machine regression, and random forest regression (RFR), were used to construct and train multiple monitoring models. Further, the four key quality parameters of tea polyphenols, total sugars, free amino acids, and caffeine content were estimated using these models. Finally, the effects of automatic and manual image background removal methods, different regression methods, and texture features on the model accuracies were compared. The results showed that the spectral characteristics of the canopy of fresh tea leaves were significantly correlated with the tea quality parameters (r ≥ 0.462). Among the sampling methods, the EXG_Ostu sampling method was best for prediction, whereas, among the models, RFR was the best fitted modeling algorithm for three of four quality parameters. The R2 and root-mean-square error values of the built model were 0.85 and 0.16, respectively. In addition, the texture features extracted from the canopy image improved the prediction accuracy of most models. This research confirms the modeling application of a combination of multi-spectral images and chemometrics, as a low-cost, fast, reliable, and nondestructive quality control method, which can effectively monitor the quality of fresh tea leaves. This provides a scientific reference for the research and development of portable tea quality monitoring equipment that has general applicability in the future.

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Section: Data Acquisition 221 Spectral Datamentioning

confidence: 99%

Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

Chen

Zhao

et al. 2021

Remote Sensing

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“…In addition, traditional hyperspectral imaging sensors can be bulky and large, which limits their portability and range of applications; however, the development of handheld spectroradiometers and small hyperspectral cameras ( Figure 4) has largely addressed this problem. While these instruments typically have a more limited spectral range than a standard hyperspectral sensor, they have the capacity to be used with real-time detection applications [51,52]. Spectroradiometers are unable to capture hyperspectral images; however, they have been used in many studies to detect plant stresses, such as peanut leaf spot disease [53] and powdery mildew in barley [52].…”

Section: Hyperspectral Imagingmentioning

confidence: 99%

Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning

Zubler

Yoon

2020

Biosensors

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Plant stresses have been monitored using the imaging or spectrometry of plant leaves in the visible (red-green-blue or RGB), near-infrared (NIR), infrared (IR), and ultraviolet (UV) wavebands, often augmented by fluorescence imaging or fluorescence spectrometry. Imaging at multiple specific wavelengths (multi-spectral imaging) or across a wide range of wavelengths (hyperspectral imaging) can provide exceptional information on plant stress and subsequent diseases. Digital cameras, thermal cameras, and optical filters have become available at a low cost in recent years, while hyperspectral cameras have become increasingly more compact and portable. Furthermore, smartphone cameras have dramatically improved in quality, making them a viable option for rapid, on-site stress detection. Due to these developments in imaging technology, plant stresses can be monitored more easily using handheld and field-deployable methods. Recent advances in machine learning algorithms have allowed for images and spectra to be analyzed and classified in a fully automated and reproducible manner, without the need for complicated image or spectrum analysis methods. This review will highlight recent advances in portable (including smartphone-based) detection methods for biotic and abiotic stresses, discuss data processing and machine learning techniques that can produce results for stress identification and classification, and suggest future directions towards the successful translation of these methods into practical use.

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“…Recently, disease prevention has become increasingly significant as a result of the demand for finer quality fiber [ 4 ]. Precision plant protection offers a non-destructive means of managing plant diseases based on the concept of spatio-temporal variability [ 5 , 6 ], and those works have inspired us to transplant new technology from the computer vision and artificial intelligence fields to detect and manage plant diseases.…”

Section: Introductionmentioning

confidence: 99%

YOLO-JD: A Deep Learning Network for Jute Diseases and Pests Detection from Images

Ahmed

et al. 2022

Plants

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Recently, disease prevention in jute plants has become an urgent topic as a result of the growing demand for finer quality fiber. This research presents a deep learning network called YOLO-JD for detecting jute diseases from images. In the main architecture of YOLO-JD, we integrated three new modules such as Sand Clock Feature Extraction Module (SCFEM), Deep Sand Clock Feature Extraction Module (DSCFEM), and Spatial Pyramid Pooling Module (SPPM) to extract image features effectively. We also built a new large-scale image dataset for jute diseases and pests with ten classes. Compared with other state-of-the-art experiments, YOLO-JD has achieved the best detection accuracy, with an average mAP of 96.63%.

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Precision Agriculture Technologies for Management of Plant Diseases

Cited by 20 publications

References 67 publications

Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning

YOLO-JD: A Deep Learning Network for Jute Diseases and Pests Detection from Images

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