Coconut

This study was conducted on the coast of Kenya, within the Kilifi Health and Demographic Surveillance System (KHDSS)
^{12 (link)–
14 (link)}. The KHDSS area was defined and mapped for demographic surveillance, clinical and epidemiological research by the KEMRI Wellcome Trust Research Programme (KWTRP) in the year 2000. It is located in Kilifi County along the coastal fringe and covers an area of 891 km
², 50 km north and south, and 30 km west, of the KCH. The KHDSS monitors a population of around 296,000 residents (2016 census) through household enumeration visits conducted every 4 months. The major economic activity of most residents is subsistence farming of maize, cassava, cashew nuts and coconuts, as well as goats and dairy cattle
^{14 (link)}.
The KHDSS area has 21 public health facilities (including the KCH) receiving out-patients, which operate under the Kenya Ministry of Health (MoH). In total, nine of these facilities were selected for this study: Matsangoni, Ngerenya, Mtondia, Sokoke, Mavueni, Jaribuni, Chasimba, Pingilikani and Junju (
Figure 1). The facilities were purposively selected to provide a broad representation across the geographical region, covering major road networks into the location and variation in population density. All specimen processing and testing was carried out at KEMRI-Wellcome Trust Research Programme laboratories in Kilifi.

The demographic history of the Seychelles warbler is outlined in Fig.1. The species was first described in 1878 by Oustalet (1878 ) from the island of Marianne (96 ha), and in the same account was said by Lantz to be ‘rare on Ile Cousine’. Subsequent studies found the warbler on Cousin, but not Cousine, and Lantz's account was presumed to be a mistake (Vesey-Fitzgerald 1940 ). By 1938, the warbler was extinct on Marianne, and Vesey-Fitzgerald (1940 ) remarked that it ‘must be the rarest [bird] in the world’. Expeditions to Cousin in 1959, 1965, 1967 and 1968 documented 30, 50, 26 and 50 individuals, respectively (Penny 1967 ; Loustau-Lalanne 1968 ). However, birds were not uniquely ringed during these trips, so these estimates of population size are unlikely to have been very precise. In 1967, Cousin was designated as a nature reserve, and efforts began to increase the populations of native bird species (Penny 1967 ). Habitat restoration, consisting of the removal of coconut palms (Cocos nucifera) to allow the succession of natural pisona (Pisonia grandis) woodland, was successful, and the Cousin warbler population quickly recovered; since the 1980s, it has been at a carrying capacity of approximately 320 adults (Brouwer et al. 2009 (link)). Between 1987 and 2011, four new warbler populations were successfully established by translocation to the islands of Aride, Cousine, Denis and Frégate (Komdeur 1994 ; Richardson et al. 2006 ; Wright et al. 2014 (link)).
Historical samples were obtained from all known Seychelles warbler museum specimens, collected from Cousin (n = 19) and Marianne (n = 7) in 1876–1940 (Table S1). Although the temporal range of sampling of the museum specimens was wide, structure analyses suggested that they grouped into two populations (see Results), enabling us to group them for population genetic analyses. A small (approximately 1.5 × 1.5 × 3.0 mm) piece of skin was excised from the ventral surface of the foot and stored at room temperature in a sterile microfuge tube. Contemporary samples were collected as part of an intensive, long-term study of Seychelles warblers on Cousin Island (Brouwer et al. 2010 (link)). Since 1988, the entire population has been extensively monitored, often in both the main (June–September) and minor (November–March) breeding seasons each year, during which birds are routinely caught with mist nets and audio lures. A blood sample (approximately 25 μL) was collected from each bird by brachial venipuncture and stored at room temperature in a screw-topped microfuge tube containing 1.5 mL absolute ethanol. Each bird was fitted with a unique combination of three colour rings and a metal British Trust for Ornithology (BTO) ring. Over 96% of adult birds on Cousin have been ringed since 1997 (Richardson et al. 2001 (link)), and a representative sampling of the population was achieved in each year. For the present analysis, 50 samples were randomly chosen from 1997 and 2011 (of 160 and 197 samples available from that year, respectively) to provide two temporally distinct contemporary population samples for comparison with the historical data.

After obtaining the CT image of the coconut, the next goal was to convert them into a point cloud model [15 ]. In MATLAB (MATLAB version: R2020b), the program first calls the ID of the target coconut, which is compressed and reconstructed into an array of 512 $\ast$ 512 $\ast$ 6. According to the Definition 1, the 6 columns are $⟨x,y,z,r,g,b⟩$ . The converted data of each image are stacked, the points are drawn according to the corresponding coordinate information values, and the 3D point cloud is displayed (Fig. 2c).

The intelligent coconut analysis system (Fig. 7) is designed according to our working method and the practical application. The Browser-Server architecture is adopted in this system, the development mode is front-end and back-end separated, and the front-end part uses Vue. JS, the development tool is VS Code, and adds the Echarts plug in library. The back-end part uses the Java language, version 1.8, uses the Springboot framework.Fig. 7

Prototype demonstration of the system

When entering the system, followed by the “import the images” function (for uploading coconut CT images), the 3D point cloud (for converting images into the 3D point cloud), contour drawing (for connecting the discrete points of a point cloud map to make it more intuitive), color rendering (applying color according to one’s preferences can help improve the look or highlight important areas), and visual presentation (for outputting the final image after the above steps). Each function block is relatively independent and can be used according to demand.
The reconstructed 3D point cloud display structure is clear at a glance. Through the algorithm, the system can extract the required internal structure and obtain the relevant data. The system provides commonly used structural categories, such as coconut meat, coconut water, germ, coconut haustorium, coconut shell, and so on. After selection, the corresponding length and diameter, long and short axis, volume and other index data can be obtained.