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Understanding the growth status of fruits can enable precise growth management and improve the product quality. Previous studies have rarely used deep learning to observe changes over time, and manual annotation is required to detect hidden regions of fruit. Thus, additional research is required for automatic annotation and tracking fruit changes over time. We propose a system to record the growth characteristics of individual apples in real time using Mask R-CNN. To accurately detect fruit regions hidden behind leaves and other fruits, we developed a region detection model by automatically generating 3000 composite orchard images using cropped images of leaves and fruits. The effectiveness of the proposed method was verified on a total of 1417 orchard images obtained from the monitoring system, tracking the size of fruits in the images. The mean absolute percentage error between the true value manually annotated from the images and detection value provided by the proposed method was less than 0.079, suggesting that the proposed method could extract fruit sizes in real time with high accuracy. Moreover, each prediction could capture a relative growth curve that closely matched the actual curve after approximately 150 elapsed days, even if a target fruit was partially hidden.

With the increasing number of cows per farmer in Japan, an automatic cow monitoring system is being introduced. One important aspect of such a system is the ability to identify individual cows and estimate their feeding time. In this study, we propose a method for achieving this goal through facial region analysis. We used a YOLO detector to extract the cow head region from video images captured during feeding with the head region cropped as a face region image. The face region image was used for cow identification and transfer learning was employed for identification. In the context of cow identification, transfer learning can be used to train a pre-existing deep neural network to recognize individual cows based on their unique physical characteristics, such as their head shape, markings, or ear tags. To estimate the time of feeding, we divided the feeding area into vertical strips for each cow and established a horizontal line just above the feeding materials to determine whether a cow was feeding or not by using Hough transform techniques. We tested our method using real-life data from a large farm, and the experimental results showed promise in achieving our objectives. This approach has the potential to diagnose diseases and movement disorders in cows and could provide valuable insights for farmers.

Red-crowned crane Grus japonensis is an endangered species in two separate populations: the mainland population in the Eurasian continent and the island population in eastern Hokkaido, Japan. We found 11 insertion/deletion (InDel) markers in the genome of the red-crowned crane and designed primer sets across these InDels that can be analyzed with conventional agarose gel electrophoresis. Sixty-six samples of whole blood and skeletal muscle obtained from red-crowned cranes, including 12 families in eastern Hokkaido from 1994 to 2021, showed different patterns in gel images of 11 InDel PCR reactions except for two pairs. The combined non-exclusion probability of the 11 markers indicates that individuals can be determined with a probability of 99.9%. In 39 non-relative chicks, the expected heterozygosity (He) was 0.316, suggesting low genetic diversity. This might not be caused by high levels of inbreeding since the average FIS was not significantly different from zero (0.095, p = 0.075). The results suggest that the 11 InDel primer sets can be used for fairly accurate individual identification as well as genetic population analyses in red-crowned cranes in the island population.

The correct identification of individuals is a requirement of capture-mark-recapture (CMR) methods, and it is commonly achieved by applying artificial marks or by mutilation of study-animals. An alternative, non-invasive method to identify individuals is to utilize the patterns of their natural body markings. However, the use of pattern mapping is not yet widespread, mainly because it is considered time consuming, particularly in large populations and/or long-term CMR studies. Here we explore the use of pattern mapping for the identification of adult individuals in the alpine (Ichthyosaura alpestris) and smooth (Lissotriton vulgaris) newts (Amphibia, Salamandridae), using the freely available, open-source software Wild-ID. Our photographic datasets comprised nearly 4000 captured animals’ images, taken during a 3-year period. The spot patterns of individual newts of both species did not change through time, and were sufficiently varied to allow their individual identification, even in the larger datasets. The pattern-recognition algorithm of Wild-ID was highly successful in identifying individual newts in both species. Our findings indicate that pattern mapping can be successfully employed for the identification of individuals in large populations of a broad range of animals that exhibit natural markings. The significance of pattern-mapping is accentuated in CMR studies that aim in obtaining long-term information on the demography and population dynamics of species of conservation interest, such as many amphibians facing population declines.

1. Capture-mark-recapture (CMR) studies provide essential information on demography, movement and other ecological characteristics of rare and endangered species. This information is required by managers to focus conservation strategies on the most relevant threats and life stages, identify critical habitat areas, and develop benchmarks for measuring success in recovery plans. However, CMR studies have been limited by individual identification methods that are not effective or practical for many types of organisms. 2. We develop a pattern recognition algorithm and photo-identification method that uses photographs taken in the field to identify individual marbled salamanders (Ambystoma opacum), using their dorsal patterns as 'fingerprints.' The algorithm ranks all images in a database against each other in order of visual similarity. We couple this technology with a graphic user interface to visually confirm or reject top-ranked algorithm results. Using this process, we analyse all adult salamander captures from one year of a long-term study. 3. In a database of 1008 images, the algorithm identified 95% of 101 known matches in the top 10 ranks (i.e. top 1% of all images). Time spent on manual elements of the matching process was estimated at one minute per image, permitting full indexing of all capture records. 4. Capture histories constructed from matched images identified 366 individuals that were captured between 2 and 5 times. Of these, less than 2% were captured at more than one of the 14 pond basins included in the study, suggesting that migrations were strongly directional to and from basins and that 'pond-shopping' among first-time breeders was infrequent. Females arrived at basins later, remained longer, and experienced more weight-loss than males during the breeding period. 5. Synthesis and applications. We develop, test, and apply a pattern recognition algorithm that enables efficient identification of individual marbled salamanders in a database exceeding 1000 images. We expect that this algorithm can be modified to facilitate individual identification in many other organisms because it does not rely on manual coding or discrete geometric pattern features. High performance results suggest that it can be scaled to larger databases, allowing biologists to address critical conservation-based questions regarding demography, reproduction and dispersal of rare and endangered species.

The decreasing cost and increasing scope and power of emerging genomic technologies are reshaping the field of molecular ecology. However, many modern genomic approaches (e.g., RAD‐seq) require large amounts of high‐quality template DNA. This poses a problem for an active branch of conservation biology: genetic monitoring using minimally invasive sampling (MIS) methods. Without handling or even observing an animal, MIS methods (e.g., collection of hair, skin, faeces) can provide genetic information on individuals or populations. Such samples typically yield low‐quality and/or quantities of DNA, restricting the type of molecular methods that can be used. Despite this limitation, genetic monitoring using MIS is an effective tool for estimating population demographic parameters and monitoring genetic diversity in natural populations. Genetic monitoring is likely to become more important in the future as many natural populations are undergoing anthropogenically driven declines, which are unlikely to abate without intensive adaptive management efforts that often include MIS approaches. Here, we profile the expanding suite of genomic methods and platforms compatible with producing genotypes from MIS, considering factors such as development costs and error rates. We evaluate how powerful new approaches will enhance our ability to investigate questions typically answered using genetic monitoring, such as estimating abundance, genetic structure and relatedness. As the field is in a period of unusually rapid transition, we also highlight the importance of legacy data sets and recommend how to address the challenges of moving between traditional and next‐generation genetic monitoring platforms. Finally, we consider how genetic monitoring could move beyond genotypes in the future. For example, assessing microbiomes or epigenetic markers could provide a greater understanding of the relationship between individuals and their environment.

Individual identification of unknown deceased is a vital function carried out by medical professionals, thus many tools have been developed or tested towards its end. One of the tools tested and still being tested is post-mortem computed tomography [PMCT]. This review aims to summarise the current state of using PMCT for personal identification beyond odontology. We found that most medicolegal researchers had a positive view of using PMCT for individual identification or for disaster victim identification. They have shown PMCT scans can be compared with a wide range of AM material – ante-mortem computed tomography [AMCT] scans, AM radiographs, or even textual AM medical history – for a successful identification. The use of textual medical history suggests the potential to create an artificial intelligence model that could quickly highlight areas of comparison. Anatomical body structures, pathological changes, or foreign bodies provide bases of comparison when using PMCT for individual identification. We found most (79 %) researchers have used qualitative methods to compare PMCT with AM material. Likewise, researchers so far have focussed on the axial skeleton (sans pelvis) when testing the viability of comparing specific body structures between AM material and PMCT scans. More body structures remain to be tested for their viability in personal identification, especially using quantitative methods. •Most medicolegal researchers have a positive opinion of using PMCT for individual identification beyond forensic odontology.•Anatomical structures, pathological changes, or foreign bodies provide bases for comparing PMCT scans with AM material.•Most researchers so far have used qualitative methods to individually ID persons when comparing PMCT scans and AM material.•So far researchers have focussed on the axial skeleton (sans pelvis) for personal ID using PMCT and AM comparison.•More body structures remain to be tested, especially using quantitative methods for better objectivity and future automation.

This work presented a literature review on animal species and individual identification tools, as well as animal monitoring capabilities. We gathered the literature to cover different aspects of technologies that are widely in use for animal identification, from the traditional up to the latest methods. This study includes species and individual animal identification attributes namely body patterns, footprints, facial features, and sound for identification purposes. The large volume of data collected could be automatically processed using machine learning and deep learning techniques to achieve both species and individual animal identification more efficiently as compared to the human workforce. It is a much faster and accurate approach considering the large volume of data, than manual processing, which is extremely expensive, time-consuming, tedious, and monotonous. We established that machine learning and advancements in deep learning hold significant promise to high-accuracy identification of both species and individual animal. Methods used for individual identification are mainly implemented in endangered species by the conservation management. The traditional methods such as the use of footprints, drawings of animal biometrics are integrated into the recent growth of technology to eliminate the human skill needed to achieve species and individual identification through the use of machine learning and deep learning algorithms for automatic identification purposes.

The individual identification of pigs is the basis for precision livestock farming (PLF), which can provide prerequisites for personalized feeding, disease monitoring, growth condition monitoring and behavior identification. Pig face recognition has the problem that pig face samples are difficult to collect and images are easily affected by the environment and body dirt. Due to this problem, we proposed a method for individual pig identification using three-dimension (3D) point clouds of the pig’s back surface. Firstly, a point cloud segmentation model based on the PointNet++ algorithm is established to segment the pig’s back point clouds from the complex background and use it as the input for individual recognition. Then, an individual pig recognition model based on the improved PointNet++LGG algorithm was constructed by increasing the adaptive global sampling radius, deepening the network structure and increasing the number of features to extract higher-dimensional features for accurate recognition of different individuals with similar body sizes. In total, 10,574 3D point cloud images of ten pigs were collected to construct the dataset. The experimental results showed that the accuracy of the individual pig identification model based on the PointNet++LGG algorithm reached 95.26%, which was 2.18%, 16.76% and 17.19% higher compared with the PointNet model, PointNet++SSG model and MSG model, respectively. Individual pig identification based on 3D point clouds of the back surface is effective. This approach is easy to integrate with functions such as body condition assessment and behavior recognition, and is conducive to the development of precision livestock farming.