Effect of planting density and complementary irrigation on the morpho-phenology, yield, profitability, and efficiency of banana fertilization

García, Galo Cedeño; Cedeño, Ángel Guzmán; Lucero, Héctor Zambrano; Macías, Leonardo Vera; López, Cristian Valdivieso; Álava, Geoconda López

doi:10.17268/sci.agropecu.2020.04.03

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Therefore, an efficient and accurate measurement method is needed to extract the phenotypic parameters of banana plants. The height and circumference of pseudo-stems are essential indicators of banana plants' growth and health status [3]. By monitoring the changes in the height and circumference of pseudo-stems, growers can assess the growth rate, growth stage, and possible growth anomalies of the plant.…”

Section: Introductionmentioning

confidence: 99%

Banana Individual Segmentation and Phenotypic Parameter Measurements Using Deep Learning and Terrestrial LiDAR

Ma,

Wang,

Zeng

et al. 2024

IEEE Access

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Banana phenotypic parameters are one of the important elements in the study of banana growth and development. Through the measurement of banana phenotypic parameters, we can obtain information about the growth status, nutritional status and quality indexes of banana plants, and pseudostem parameters are significant indicators in banana phenotypic parameters. This research proposes a twostage approach combining morphological features and deep learning point cloud segmentation to extract banana pseudo-stem parameters. Specifically, in the first step, seed points are extracted using the DBSCAN clustering algorithm, and banana individual plant segmentation is accomplished using the region growing algorithm based on seed points. Its precision, recall and F1-score were 97.73%, 97.36% and 97.54%, respectively. This indicates that the DBSCAN clustering algorithm and the seed point based region growing algorithm can effectively realize the plant count of banana plants and initially realize the individual plant segmentation of banana. The second step is to use PointNet++, PointNet, and DGCNN for pseudo-stem and canopy segmentation of individual banana plants. All three models perform well in segmentation, with PointNet++ performing the best. Its precision, recall, F1-score, Matthews correlation coefficient and Dice coefficient reached 0.9956, 0.9709, 0.9831, 0.9670 and 0.9831. this shows that deep learning has a better applicability in segmenting banana plants. In the results of segmentation, we measure the banana pseudostem circumference and pseudo-stem height. The correlation between the extracted pseudo-stem height and pseudo-stem circumference compared to the measured values was 96.70% and 82.32%, respectively. The above two-stage method of extracting banana pseudo-stem parameters overcomes the difficulties of point cloud individual plant segmentation associated with intensive banana cultivation. It makes the management of individual banana plants possible and provides accurate phenotypic parameter information for banana plantation management. It lays the foundation for further assessment of banana growth and nutritional status.

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Section: Introductionmentioning

confidence: 99%

Banana Individual Segmentation and Phenotypic Parameter Measurements Using Deep Learning and Terrestrial LiDAR

Ma,

Wang,

Zeng

et al. 2024

IEEE Access

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“…Adult banana plants consist of components such as corms, pseudo-stems, leaves, and fruit clusters. The diameter and height of the banana plant pseudo-stem are crucial indicators for assessing plant health and predicting banana yields- ( García et al., 2020 ; Magalhães et al., 2020 ). Accurately and rapidly measuring the pseudo-stem diameter and height is of paramount importance for evaluating banana growth and providing scientific guidance for field management.…”

Section: Introductionmentioning

confidence: 99%

A method for measuring banana pseudo-stem phenotypic parameters based on handheld mobile LiDAR and IMU fusion

Yang,

Jiang,

Duan

et al. 2024

Front. Plant Sci.

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Diameter and height are crucial morphological parameters of banana pseudo-stems, serving as indicators of the plant’s growth status. Currently, in densely cultivated banana plantations, there is a lack of applicable research methods for the scalable measurement of phenotypic parameters such as diameter and height of banana pseudo-stems. This paper introduces a handheld mobile LiDAR and Inertial Measurement Unit (IMU)-fused laser scanning system designed for measuring phenotypic parameters of banana pseudo-stems within banana orchards. To address the challenges posed by dense canopy cover in banana orchards, a distance-weighted feature extraction method is proposed. This method, coupled with Lidar-IMU integration, constructs a three-dimensional point cloud map of the banana plantation area. To overcome difficulties in segmenting individual banana plants in complex environments, a combined segmentation approach is proposed, involving Euclidean clustering, Kmeans clustering, and threshold segmentation. A sliding window recognition method is presented to determine the connection points between pseudo-stems and leaves, mitigating issues caused by crown closure and heavy leaf overlap. Experimental results in banana orchards demonstrate that, compared with manual measurements, the mean absolute errors and relative errors for banana pseudo-stem diameter and height are 0.2127 cm (4.06%) and 3.52 cm (1.91%), respectively. These findings indicate that the proposed method is suitable for scalable measurements of banana pseudo-stem diameter and height in complex, obscured environments, providing a rapid and accurate inter-orchard measurement approach for banana plantation managers.

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“…The banana industry is an important pillar for rural economic prosperity and farmers’ poverty alleviation in many areas of southern China [ 1 , 2 ]. The physical parameters of banana plant number, pseudo-stem diameter, and height are important morphological and phenotypic parameters of banana plants, and are closely related to the growth and yield of banana [ 3 , 4 ]. Therefore, the measurement of morphological and phenotypic parameters is of great significance for banana production and field management.…”

Section: Introductionmentioning

confidence: 99%

Banana plant counting and morphological parameters measurement based on terrestrial laser scanning

et al. 2022

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Background The number of banana plants is closely related to banana yield. The diameter and height of the pseudo-stem are important morphological parameters of banana plants, which can reflect the growth status and vitality. To address the problems of high labor intensity and subjectivity in traditional measurement methods, a fast measurement method for banana plant count, pseudo-stem diameter, and height based on terrestrial laser scanning (TLS) was proposed. Results First, during the nutritional growth period of banana, three-dimensional (3D) point cloud data of two measured fields were obtained by TLS. Second, the point cloud data was preprocessed. And the single plant segmentation of the canopy closed banana plant point cloud was realized furtherly. Finally, the number of banana plants was obtained by counting the number of pseudo-stems, and the diameter of pseudo-stems was measured using a cylindrical segmentation algorithm. A sliding window recognition method was proposed to determine the junction position between leaves and pseudo-stems, and the height of the pseudo-stems was measured. Compared with the measured value of artificial point cloud, when counting the number of banana plants, the precision,recall and percentage error of field 1 were 93.51%, 94.02%, and 0.54% respectively; the precision,recall and percentage error of field 2 were 96.34%, 92.00%, and 4.5% respectively; In the measurement of pseudo-stem diameter and height of banana, the root mean square error (RMSE) of pseudo-stem diameter and height of banana plant in field 1 were 0.38 cm and 0.2014 m respectively, and the mean absolute percentage error (MAPE) were 1.30% and 5.11% respectively; the RMSE of pseudo-stem diameter and height of banana plant in field 2 were 0.39 cm and 0.2788 m respectively, and the MAPE were 1.04% and 9.40% respectively. Conclusion The results show that the method proposed in this paper is suitable for the field measurement of banana count, pseudo-stem diameter, and height and can provide a fast field measurement method for banana plantation management.

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Effect of planting density and complementary irrigation on the morpho-phenology, yield, profitability, and efficiency of banana fertilization

Cited by 5 publications

References 3 publications

Banana Individual Segmentation and Phenotypic Parameter Measurements Using Deep Learning and Terrestrial LiDAR

Banana Individual Segmentation and Phenotypic Parameter Measurements Using Deep Learning and Terrestrial LiDAR

A method for measuring banana pseudo-stem phenotypic parameters based on handheld mobile LiDAR and IMU fusion

Banana plant counting and morphological parameters measurement based on terrestrial laser scanning

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