Identification of Abandoned Jujube Fields Using Multi-Temporal High-Resolution Imagery and Machine Learning

Li, Xingrong; Yang, Chenghai; Zhang, Hongri; Wang, Panpan; Tang, Jia; Tian, Yanqin; Zhang, Qing

doi:10.3390/rs13040801

Cited by 8 publications

(4 citation statements)

References 56 publications

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“…The 224th regiment, the study area selected for this experiment, is located north of National Highway 315 at the crossroads of Pishan County and Moyu County in Hotan Region, on the southern edge of the Great Taklamakan Desert in Xinjiang, China (Li et al, 2021). The total land area is 234,751 km 2 and the terrain slopes from the southwest to the northeast.…”

Section: Study Areasmentioning

confidence: 99%

“…Satellite remote sensing is primarily used for monitoring broad areas, but it cannot provide images with sufficient spatial resolution and the images are susceptible to weather conditions (Bendig et al, 2015;You et al, 2022). In addition, the progressive improvement of UAV technology has made feasible its combination with hyperspectral and multispectral technology for agricultural disease monitoring, providing a reference for accurate crop disease monitoring and to guide remedial management (Adao et al, 2017;Li et al, 2021). For instance, UAV hyperspectral remote sensing can monitor a broad area with high precision, efficiency, and continuity, and accomplish the fusion of UAV multisource remote sensing imagery and target extraction.…”

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: Study Areasmentioning

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.

Self Cite

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show abstract

“…Similarly, in ResNet, we made modifications and additions to the sequential model structure, ultimately achieving positive outcomes. Xingrong Li et al identified abandoned jujube orchards with an accuracy of 91.1% using multi-temporal high-resolution imagery and machine learning techniques, showing a significant enhancement in accuracy compared to other moderate-resolution satellite images [60]. However, our experimental approach, employing CNN-based models, clearly demonstrated superior performance compared to these established traditional methods and previous research findings.…”

Section: Discussionmentioning

confidence: 56%

Research on Walnut (Juglans regia L.) Classification Based on Convolutional Neural Networks and Landsat-8 Remote Sensing Imagery

Wu,

Li,

Shi

et al. 2024

Forests

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The study explores the use of convolutional neural networks (CNNs) and satellite remote sensing imagery for walnut analysis in Ganquan Township, Alar City, Xinjiang. The recent growth of walnut cultivation in Xinjiang presents challenges for manual data collection, making satellite imagery and computer vision algorithms a practical solution. Landsat-8 satellite images from Google Earth Engine underwent preprocessing, and experiments were conducted to enhance the ResNet model, resulting in improved accuracy and efficiency. Experiments were conducted to evaluate multiple CNN models and traditional methods, and the best detection method was chosen through comparisons. A comparison was drawn between traditional algorithms and convolutional neural network algorithms based on metrics such as precision, recall, f1-score, accuracy, and total time. The results indicated that although traditional methods were more efficient compared to CNN, they exhibited lower accuracy. In the context of this research, prioritizing efficiency at the cost of accuracy was deemed undesirable. Among the traditional algorithms employed in this study, k-NN produced the most favorable outcomes, with precision, recall, f1-score, and accuracy reaching 75.78%, 92.43%, 83.28%, and 84.46%, respectively, although these values were relatively lower than those of the CNN algorithm models. Within the CNN models, the ResNet model demonstrated superior performance, yielding corresponding results of 92.47%, 94.29%, 93.37%, and 93.27%. The EfficientNetV2 model also displayed commendable results, with precision, recall, and f1-score achieving 96.35%, 91.44%, and 93.83%. Nevertheless, it is worth noting that the classification efficiency of EfficientNetV2 fell significantly short of that of ResNet. Consequently, in this study, the ResNet model proved to be relatively more effective. Once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. Through our studies, we discovered that once optimized, the most efficient CNN model closely rivals traditional algorithms in terms of time efficiency for generating results while significantly surpassing them in accuracy. In this study, empirical evidence demonstrates that integrating CNN-based methods with satellite remote sensing technology can effectively enhance the statistical efficiency of agriculture and forestry sectors, thus leading to substantial reductions in operational costs. These findings lay a solid foundation for further research in this field and offer valuable insights for other agricultural and forestry-related studies.

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“…Morell-Monzó et al [12] used Sentinel-2 and airborne imagery to map land abandonment (citrus cultivation) in fragmented landscapes in Spain, applying the Random Forest algorithm for the classification of pixels. Li et al [16] applied field-based and pixel-based classification of field boundaries to estimate the percentage of abandoned jujube fields with multi-temporal high spatial resolution satellite images (Gaofen-1 and Gaofen-6) and the Random Forest algorithm.…”

Section: Introductionmentioning

confidence: 99%

Detecting Olive Grove Abandonment with Sentinel-2 and Machine Learning: The Development of a Web-Based Tool for Land Management

Volpi¹,

Marchi²,

Petacchi³

et al. 2022

SSRN Journal

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Identification of Abandoned Jujube Fields Using Multi-Temporal High-Resolution Imagery and Machine Learning

Cited by 8 publications

References 56 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

Research on Walnut (Juglans regia L.) Classification Based on Convolutional Neural Networks and Landsat-8 Remote Sensing Imagery

Detecting Olive Grove Abandonment with Sentinel-2 and Machine Learning: The Development of a Web-Based Tool for Land Management

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