Improving Coconut Using Modern Breeding Technologies: Challenges and Opportunities

Characterizations of Ganoderma species causing basal stem rot disease in coconut tree

The basal stem rot disease incidence ranged from 0 to 5% in Karnataka India during the year 2019-20. Twenty pathogenic isolates of Ganoderma sp varied with cultural characteristics and virulence on coconut seedlings of the variety Tipatur Tall. The identity of each isolate was confirmed through morphological characters and through ITS sequencing. Two isolates viz., G4 and G5 were identified as Ganoderma applanatum and remaining all isolates were identified as G. lucidum. The genetic diversity analysis of Ganoderma isolates was done using ten Random Amplified Polymorphic DNA (RAPD) and fifteen Inter Simple Sequence Repeat (ISSR) primers. Among the ten RAPD primers, only eight primers recorded polymorphism (33.30-66.70%). The primer SBS-Q3 exhibited the highest polymorphism of 66.70%. In case of ISSR primers, all primers recorded polymorphism (33.30-60.00%). The primer UBC866 was the most polymorphic primer with 60.0% polymorphism. RAPD and ISSR markers were compared for their efficacy in assessing the genetic diversity by taking the band frequency, Shannon's index, polymorphic information content, resolving power, and mean resolving power into consideration, and it was concluded that ISSR was marker of choice over RAPD.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03872-w.

An improved Deeplab V3+ network based coconut CT image segmentation method

Due to the unique structure of coconuts, their cultivation heavily relies on manual experience, making it difficult to accurately and timely observe their internal characteristics. This limitation severely hinders the optimization of coconut breeding. To address this issue, we propose a new model based on the improved architecture of Deeplab V3+. We replace the original ASPP(Atrous Spatial Pyramid Pooling) structure with a dense atrous spatial pyramid pooling module and introduce CBAM(Convolutional Block Attention Module). This approach resolves the issue of information loss due to sparse sampling and effectively captures global features. Additionally, we embed a RRM(residual refinement module) after the output level of the decoder to optimize boundary information between organs. Multiple model comparisons and ablation experiments are conducted, demonstrating that the improved segmentation algorithm achieves higher accuracy when dealing with diverse coconut organ CT(Computed Tomography) images. Our work provides a new solution for accurately segmenting internal coconut organs, which facilitates scientific decision-making for coconut researchers at different stages of growth.

Developing non-invasive 3D quantificational imaging for intelligent coconut analysis system with X-ray

BACKGROUND

As one of the largest drupes in the world, the coconut has a special multilayered structure and a seed development process that is not yet fully understood. On the one hand, the special structure of the coconut pericarp prevents the development of external damage to the coconut fruit, and on the other hand, the thickness of the coconut shell makes it difficult to observe the development of bacteria inside it. In addition, coconut takes about 1 year to progress from pollination to maturity. During the long development process, coconut development is vulnerable to natural disasters, cold waves, typhoons, etc. Therefore, nondestructive observation of the internal development process remains a highly important and challenging task. In this study, We proposed an intelligent system for building a three-dimensional (3D) quantitative imaging model of coconut fruit using Computed Tomography (CT) images. Cross-sectional images of coconut fruit were obtained by spiral CT scanning. Then a point cloud model was built by extracting 3D coordinate data and RGB values. The point cloud model was denoised using the cluster denoising method. Finally, a 3D quantitative model of a coconut fruit was established.

RESULTS

The innovations of this work are as follows. 1) Using CT scans, we obtained a total of 37,950 non-destructive internal growth change maps of various types of coconuts to establish a coconut data set called "CCID", which provides powerful graphical data support for coconut research. 2) Based on this data set, we built a coconut intelligence system. By inputting a batch of coconut images into a 3D point cloud map, the internal structure information can be ascertained, the entire contour can be drawn and rendered according to need, and the long diameter, short diameter and volume of the required structure can be obtained. We maintained quantitative observation on a batch of local Hainan coconuts for more than 3 months. With 40 coconuts as test cases, the high accuracy of the model generated by the system is proven. The system has a good application value and broad popularization prospects in the cultivation and optimization of coconut fruit.

CONCLUSION

The evaluation results show that the 3D quantitative imaging model has high accuracy in capturing the internal development process of coconut fruits. The system can effectively assist growers in internal developmental observations and in structural data acquisition from coconut, thus providing decision-making support for improving the cultivation conditions of coconuts.