A New Soybean Ndvi Data-Based Partitioning Algorithm for Fertilization Management Zoning

Chen, H.; Wang, X.; Zhang, W.; Wang, X.Z.; Di, Xu; La, Qi

doi:10.15666/aeer/1902_13911405

Cited by 3 publications

(6 citation statements)

References 25 publications

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“…They reported that when the data volume was greater than 8000, the lowest silhouette coefficient was 0.543, indicating good clustering performance. Through an improved algorithm, when the number of partitions was 4, the silhouette coefficient was only 0.537, indicating better clustering performance than that of Chen et al (2021). The NDVI acquisition and management zoning methods in this study based on near-surface remote sensing are different from those in other studies, such as those involving management zoning methods based on drone remote sensing and the traditional preparation of physical maps, which can involve complex processes such as large-scale crop sampling.…”

Section: Discussionmentioning

confidence: 74%

Method for Zoning Corn Based on the NDVI and the Improved SOM-K-Means Algorithm

Di,

Wang

2023

Journal of the ASABE

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HIGHLIGHTS• A partitioning method based on the NDVI and improved SOM-K-MEANS algorithm is proposed.• The optimal number of partitions is determined according to the DBI, silhouette coefficient and silhouette analysis.• This method provides a new approach for the real-time variable-rate fertilization of maize. ABSTRACT.To solve problems such as low nitrogen use efficiency during corn intertillage and topdressing and the presence of spatial differences in corn growth, a method for zoning and division based on the normalized difference vegetation index (NDVI) and the improved self-organizing map (SOM)-K-means algorithm was proposed. First, the GreenSeeker spectrum sensor was utilized to acquire the NDVI of the corn canopy in the vegetative V6-V10 stage during the intertillage period. Second, the acquired data were screened, and the SOM-K-means algorithm was used to perform a cluster analysis of the processed data. Finally, the clustering performance was analyzed. The initial clustering center was acquired with an SOM neural network, the clustering center was used in K-means clustering, and zoning was performed. The optimal number of zones was 4 according to the Davies-Boulding Index (DBI), the silhouette coefficient, and a silhouette analysis of the differences in corn growth. With four zones, the DBI and the silhouette coefficient were 0.569 and 0.537, respectively. Clusters 1 and 4 and clusters 2 and 3 in the silhouette map displayed similar thicknesses, with large differences between clusters and small differences within clusters. A comparison of the SOM-K-means algorithm, K-means algorithm, and SOM neural network algorithm revealed that the run time of the SOM-K-means clustering algorithm was 4.880 s, its inertia was 9.2118, and the run time of the SOM neural network was 4.715 s. The overall coefficient of variation of corn growth in the unzoned test area was 15.24%, and the overall coefficient of variation of corn growth after zoning was 6.94%. This method provides a new approach for understanding issues related to variable-rate fertilization zoning and online real-time zoning during corn intertillage based on the NDVI.

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Section: Discussionmentioning

confidence: 74%

Method for Zoning Corn Based on the NDVI and the Improved SOM-K-Means Algorithm

Di,

Wang

2023

Journal of the ASABE

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“…In summary, SHGD in ACCSH can mine the spatial heterogeneous characteristics of crop growth. To further demonstrate the efficacy of SHGD, we use the typical layered approaches, such as that based on management zones [7,8] and on segmented single climatic variables (i.e., temperature or precipitation) in place of SHGD in the ACCSH. The results in Figure 14 indicate that ACCSH based on SHGD has the highest classification accuracy, demonstrating its power to mine spatial heterogeneous patterns for crop classification.…”

Section: Analysis Of the Spatial Heterogeneity Patterns Of Crop Growthmentioning

confidence: 99%

“…Remote Sens. 2023, 15, 5550 2 of 24 Several researchers proposed layered strategies [4][5][6][7][8] to address spatial heterogeneity's influence on crop classification, which can be generally divided into two types: methods based on climatic conditions and those based on management zones. Hao et al [4] divided China into eight zones based on its geographic position and climate and established classification guidelines for each zone.…”

Section: Introductionmentioning

confidence: 99%

“…Aparecido et al [6] divided their study area into sub-regions according to segmentation temperature and precipitation. NDVI index segments were used by Chen et al [7] and Santos et al [8] to create management zones for multilayer crop classification. The zoning classification strategy [4][5][6][7][8] can also successfully reduce the effect of spatial heterogeneity on crop classification.…”

Section: Introductionmentioning

confidence: 99%

“…NDVI index segments were used by Chen et al [7] and Santos et al [8] to create management zones for multilayer crop classification. The zoning classification strategy [4][5][6][7][8] can also successfully reduce the effect of spatial heterogeneity on crop classification. The accuracy of crop recognition based on spatially homogeneous zones is higher than that of the global scale.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Crop Classification Based on Optimized Spectral and Textural Indexes Considering Spatial Heterogeneity

Wang,

Liu,

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et al. 2023

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Crop recognition with high accuracy at a large scale is hampered by the spatial heterogeneity of crop growth characteristics under the complex influence of environmental conditions. With the aim to automatically realize large-scale crop classification with high accuracy, this study proposes an automatic crop classification strategy considering spatial heterogeneity (ACCSH) by combining the geographic detector technique, random forest average accuracy model, and random forest classification model. In ACCSH, spectral and textural indexes that can quantify crop growth characteristics and environmental variables with potential driving effects are first calculated. Next, an adaptive spatial heterogeneity mining method based on the geographic detector technique is proposed to mine spatial homogeneous zones adaptively with significant differentiation of crop growth characteristics. Subsequently, in view of the differences in crop growth characteristics and key classification indexes between spatial homogeneous zones, correlation analysis, and random forest average accuracy are combined to optimize classification indexes independently within each zone. Finally, random forest is used to classify the target crop in each spatial homogeneous zone separately. The proposed ACCSH is applied to automatically recognize crop types, specifically wheat and corn, in northern France. Results show that kappa coefficients of wheat and corn using ACCSH are 15% and 26% higher than those of classifications at the global scale, respectively. In addition, the index optimization strategy in ACCSH shows apparent superiority. Kappa coefficients of wheat and corn are 5–18% and 9–42% higher than those of classifications based on non-optimized indexes, respectively. In general, ACCSH can automatically realize crop classification with a high precision that suggests its reliability.

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Forming and Degradation Mechanism of Bowl Seedling Tray Based on Straw Lignin Conversion

Zhang

et al. 2023

Agronomy

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In response to the problems of low straw utilization efficiency and poor returning effect in Northeast China, this paper takes rice straw containing cow dung as the experimental material, and according to the characteristics of lignin glass transformation of the material, proposes a new method to prepare biomass seedling trays. The seedling trays prepared by this method can meet the needs of corn seedling cultivation and transplantation. To study the molding mechanism, scanning electron microscopy and a universal testing machine were used to compare the changes in the internal structure and mechanical properties of the regularly- and hot-compressed seedling trays before and after seedling raising. The results show that the material with water content of 23% has the best hot-pressing effect. The forming mechanism is: that the strength of the molded seedling tray resulted from the mechanical setting force of the multilayered stem fibers with a mosaic structure within the seedling tray. The adhesion and wrapping by lignin prevented water penetration from damaging the multilayered stem fibers and slightly improved their strength. The seedling tray made of straw and manure was completely degraded over 40 days, and the straw degradation rate was improved. This method can increase the overall quality and benefits of straw, providing a foundational reference for high-quality and high-efficiency straw utilization.

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A New Soybean Ndvi Data-Based Partitioning Algorithm for Fertilization Management Zoning

Cited by 3 publications

References 25 publications

Method for Zoning Corn Based on the NDVI and the Improved SOM-K-Means Algorithm

Method for Zoning Corn Based on the NDVI and the Improved SOM-K-Means Algorithm

Automatic Crop Classification Based on Optimized Spectral and Textural Indexes Considering Spatial Heterogeneity

Forming and Degradation Mechanism of Bowl Seedling Tray Based on Straw Lignin Conversion

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