Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors

Lei, Shuhan; Luo, Jianbiao; Yang, Xiaoping; Qiu, Zixuan

doi:10.3390/rs13224562

Cited by 31 publications

(25 citation statements)

References 48 publications

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“…For identification of agricultural pests and diseases, the visible red band, which is highly absorptive in green plants, and the nearinfrared band, which is highly reflective and transmissive in green plants, are often selected. The spectral response of these two bands to the same biophysical phenomena provides a strong contrast that changes with the leaf canopy structure and coverage; hence, their ratio, difference, or linear combination may be utilized to augment or disclose the implicit vegetation information (Lei et al, 2021). In the present study, the normalized difference vegetation index (NDVI), ratio vegetation index (RVI), physiological reflex vegetation index (PhRI), modified chlorophyll absorption ratio index (MCARI), transformed chlorophyll absorption ratio index (TCARI), and green index (GI) were chosen.…”

Section: Vegetation Indexmentioning

confidence: 99%

“…UAV hyperspectral remote sensing facilitates information extraction in image and spectral dimensions, and is frequently employed for monitoring agricultural growth conditions, and pest and disease stress in the field. Photosynthesis is an essential reference for evaluation of plant development (Hunt et al, 2013, Sun Q. et al, 2021, and chlorophyll content is an indication of plant photosynthetic capacity; hence, chlorophyll content can effectively reflect the growth status of a crop (Ji et al, 2021;Kaivosoja et al, 2021;Lei et al, 2021). The variation of the chlorophyll content of crops is important for monitoring the growth of crops.…”

Section: Introductionmentioning

confidence: 99%

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Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

Qiu²,

Zhang³

et al. 2022

Front. Plant Sci.

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During the growth season, jujube trees are susceptible to infestation by the leaf mite, which reduces the fruit quality and productivity. Traditional monitoring techniques for mites are time-consuming, difficult, subjective, and result in a time lag. In this study, the method based on a particle swarm optimization (PSO) algorithm extreme learning machine for estimation of leaf chlorophyll content (SPAD) under leaf mite infestation in jujube was proposed. Initially, image data and SPAD values for jujube orchards under four severities of leaf mite infestation were collected for analysis. Six vegetation indices and SPAD value were chosen for correlation analysis to establish the estimation model for SPAD and the vegetation indices. To address the influence of colinearity between spectral bands, the feature band with the highest correlation coefficient was retrieved first using the successive projection algorithm. In the modeling process, the PSO correlation coefficient was initialized with the convergent optimal approximation of the fitness function value; the root mean square error (RMSE) of the predicted and measured values was derived as an indicator of PSO goodness-of-fit to solve the problems of ELM model weights, threshold randomness, and uncertainty of network parameters; and finally, an iterative update method was used to determine the particle fitness value to optimize the minimum error or iteration number. The results reflected that significant differences were observed in the spectral reflectance of the jujube canopy corresponding with the severity of leaf mite infestation, and the infestation severity was negatively correlated with the SPAD value of jujube leaves. The selected vegetation indices NDVI, RVI, PhRI, and MCARI were positively correlated with SPAD, whereas TCARI and GI were negatively correlated with SPAD. The accuracy of the optimized PSO-ELM model (R2 = 0.856, RMSE = 0.796) was superior to that of the ELM model alone (R2 = 0.748, RMSE = 1.689). The PSO-ELM model for remote sensing estimation of relative leaf chlorophyll content of jujube shows high fault tolerance and improved data-processing efficiency. The results provide a reference for the utility of UAV remote sensing for monitoring leaf mite infestation of jujube.

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Section: Vegetation Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

Qiu²,

Zhang³

et al. 2022

Front. Plant Sci.

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“…Implementation of Unmanned Aerial Vehicle (UAV) technology helps monitor plant health on plantations quickly, effectively, and efficiently [9]. Most of the studies identified canopy density based on the vegetation index on multispectral satellite images.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approach to assess rubber plant health through canopy density mapping using very high-resolution aerial photographs

Ayu,

Manessa,

Stevanus

et al. 2024

Eighth Geoinformation Science Symposium 2023: Geoinformation Science for Sustainable Planet

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The health of Indonesian rubber plantations has recently been compromised by rubber leaf fall disease, prompting a need for effective monitoring techniques. This study explores the use of high-resolution aerial photographs to assess rubber plant health through canopy density analysis. We employed the Random Forest machine learning algorithm for this purpose, focusing on two classification systems: [low, medium, high] and [low, high] canopy densities. Our findings reveal contrasting levels of accuracy between the two classification systems. The three-tier classification ([low, medium, high]) resulted in moderate accuracy (Overall Accuracy: 0.50, Kappa Value: 0.24), suggesting that this approach might be too detailed for the task. In contrast, the binary classification ([low, high]) demonstrated significantly better performance, with satisfactory accuracy (Overall Accuracy: 0.76, Kappa Value: 0.33). This improvement indicates that a simpler classification system with fewer categories is more effective for identifying the health of rubber plants using aerial photographs and machine learning techniques. This study underscores the importance of selecting an appropriate level of classification detail in machine learning models for agricultural monitoring. The results suggest that less complex models, with fewer canopy density categories, are more suitable for accurately assessing the health of rubber plants in situations like the rubber leaf fall disease outbreak in Indonesia.

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“…Kanan and Kumar [9] proposed the method where comparison between the data values are done by using a regression algorithm to find similarities in the pattern of conditions using IoT sensors. Lei et al [10] focused on using many machine learning algorithms like back propagation in neural network (BPNN), decision tree (DT), naive Bayes and k-nearest neighbors algorithm (KNN). A disease detection and classification system based on Android was proposed by Tlhobogang and Wannous [11].…”

Section: Introductionmentioning

confidence: 99%

Identification and categorization of diseases in arecanut: a machine learning approach

Hegde

Sadanand

Hegde

et al. 2023

IJEECS

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Arecanut is one of the prominent commercial crops that are grown worldwide for traditional medicines, furniture, cosmetics, food, veterinary preparations, and textile industries. It experiences a variety of diseases during its existence, from the bottom to the tip. The conventional method for detection of diseases is through visual inspection and it is also necessary to have properly designed laboratories to check these harvests. It is a time consuming and tedious task to inspect these crops across wide acres of plantations. The proposed system has been developed that uses convolutional neural network (CNN) to identify and categorize diseases in arecanuts, trunks and leaves also suggesting effective preventative measures. Proprietary dataset consists of 1,100 photos of healthy and diseased arecas. The ratio between the train and test data is 80:20. Binary cross entropy is employed as the loss function for model construction, with accuracy serving as the metrics and Adam serving as the optimizing function. In identification and categorization of arecanut diseases, the suggested approach was shown to be efficient with 93.05% accuracy.

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Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors

Cited by 31 publications

References 48 publications

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

Inversion of chlorophyll content under the stress of leaf mite for jujube based on model PSO-ELM method

Machine learning approach to assess rubber plant health through canopy density mapping using very high-resolution aerial photographs

Identification and categorization of diseases in arecanut: a machine learning approach

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