Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture. Recent advancements in high-throughput phenotyping technologies and artificial intelligence (AI) have revolutionized the field, enabling rapid and accurate assessment of crop traits on a large scale. The integration of AI and machine learning algorithms with high-throughput phenotyping data has unlocked new opportunities for crop improvement. AI algorithms can analyze and interpret large datasets, extracting meaningful patterns and correlations between phenotypic traits and genetic factors. These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection, reducing the time and cost required for variety development. However, further research and collaborations are needed to overcome the challenges and fully unlock the power of high-throughput phenotyping and AI in crop improvement. By leveraging AI algorithms, researchers can efficiently analyze phenotypic data, uncover complex patterns, and establish predictive models that enable precise trait selection and crop breeding. The aim of this review is to explore the transformative potential of integrating high-throughput phenotyping and AI in crop improvement. The review will encompass an in-depth analysis of recent advancements and applications, highlighting the numerous benefits and challenges associated with high-throughput phenotyping and intelligence. 

Integrating artificial intelligence and high-throughput phenotyping for crop improvement

Comparative study of lipid nanoparticle-based mRNA vaccine bioprocess with machine learning and combinatorial artificial neural network-design of experiment approach.

Abstract As the challenges of food insecurity and population explosion become more pressing, there is a dire need to revamp the existing breeding and animal management systems. This can be achieved by the introduction of technology for efficiency and the improvement of the genetic merit of animals. A fundamental requirement for animal breeding is the availability of accurate and reliable pedigreed data and tools facilitating sophisticated computations. Keeping this in view, Smart Sheep Breeder (SSB) was developed using the waterfall methodology and multiple programming languages. It is a multi-use online artificial intelligence (AI) based and internet of things (IoT) compatible decision support system (DSS). It is capable of automatic performance recording, farm data management, data mining, biometrical analysis, e-governance, and decision-making in sheep farms. A centralized database was also developed capable of ranking sheep across multiple farms based on genetic merit and effective dissemination of germplasm. The system in India is available as a web-based tool and android application which facilitates performance recording and generates customized reports on various aspects of sheep production. SSB uses artificial intelligence and biometrical genetic algorithms to calculate breeding values, and inbreeding coefficients, construct selection indices and generate pedigree, and history sheets as well as more than 40 types of custom-tailored animal and farm reports and graphs. The algorithms used were validated using on farms using farm data and also by comparison with established methods and software. Smart Sheep Breeder could thus prove to be indispensable for the present farming systems which could be used by sheep farm managers and breeders across India. 

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Development of a multi-use decision support system for scientific management and breeding of sheep

Black Bengal goat (BBG) is the most widely recognized legacy goat breed in Bangladesh. The breed is black in color yet likewise earthy, white, or dim colors additionally found. The breed has medium body size with grown-up weight 25–30 kg, little horns, short legs, and tight body structure. The BBG is one of the most compliant, all around adjusted, early maturing, prolific, productive, and tropical disease–resistant goat types of the world that produces incredible quality meat, milk, and skin. The breed is versatile in hot, moist, cruel, climatic conditions and flourishes well on a cacophonous dietary regimen from uncultivable decrepit grounds, residences, riversides, banks, sloping, and hilly territories where crop culture or dairy nourishing is inconceivable. In Bangladesh, the BBG is one of the main red meat–producing small ruminants which shares remarkable local interest during Eid-Ul-Adha, Eid-Ul-Fitr, wedding ceremony, birthday festival, circumcision, memorial programs, and other social celebrations with no social, cultural, and religious limitations. Being little in size, the BBG has been an amazing asset to advance supportable vocations for the negligible, little, and landless ranchers who rely upon free regular grazing lands for raising domesticated animals. Regardless of incredibly exceptional components and features, the production of BBG has not yet been popularized widely since meager consideration has been paid for improving their efficiency. Development of cutting edge hereditary, dietary, and health as well as disease control procedure and utilization of modern management frameworks may procure considerable changes in improving the overall performance of the BBG.

Performance of Black Bengal goat: a 50-year review

Abstract Background With the advancement in technology the amount of data generated, in almost every sphere of life, is increasing exponentially. This enormous amount of data needs new powerful tools for analysis and inference drawing. One such process is data mining which is the automated extraction of hidden, previously unknown, and useful knowledge from big data. Data mining is crucial as conventional strategies cannot keep up with the analysis of rapidly accumulating data and they are also inflexible in the wake of new challenges. Animal sciences are no exception to the changing scenario, especially when animal farms are quickly becoming more data intensive. Main body of the abstract The amount of data generated on the farms is also growing exponentially as farms become more intensive and mechanized. There is thus a need to utilize the knowledge of multidisciplinary fields like advanced statistics, artificial intelligence, machine learning, and database management, for revamping animal sciences. Artificial neural networks (ANNs) offer a lot of promise in this direction since they are motivated by the distributed, massively parallel computation in the brain. ANNs are powerful machine learning tools that offer multiple advantages for data mining over traditional techniques in being fast, accurate, self-organizing, robust, and highly accepting of noisy and imprecise data. Neural networks are being applied successfully for a myriad of supervised and unsupervised learning applications to draw useful hitherto unknown inferences, patterns, and relationships. Neural networks have been used successfully for pattern recognition, clustering, forecasting, prediction, and classification in animal sciences due to their capacity to learn from data, their nonparametric nature, and their ability to generalize well. Today ANN computing is a major element within any data mining tool kit. Popular methods used for neural network computing include feed-forward networks, feedback networks, and self-organization networks. ANN also offers powerful and distributed computing architecture, especially under a scenario where the data are readily available in significant quantity. Short conclusion This paper gives an overview of ANN and their applications in animal sciences and reviews major research conducted in this new and exciting area of artificial intelligence. Research in many aspects of ANN in Animal Sciences has been conducted globally although there is scope for more research in aspects of animal health, monitoring, breeding as well as nutrition . 

/pdf/artificial-neural-networks-for-data-mining-in-animal-315ey59x.pdf

Artificial neural networks for data mining in animal sciences

Background: Artificial intelligence (AI) is transforming all spheres of life and it has the potential to revolutionize animal husbandry as well. In this regard, an attempt was made to compare two AI techniques for predicting 12-month body weights of animals; viz. Principal Component regression (PCR) and Ordinary Least Squares (OLS) for datasets of Corriedale sheep spanning 11 years. Methods: PCR models were trained by varying proportions of training and testing datasets. The dataset was subject to PCR before analysis and tested (PCA dataset). A separate dataset was also created by feature selection of the PCA (PCA+FS dataset) variables.
Result: The highest correlation coefficients between test and predicted variables for two datasets (PCA dataset and PCA+FS dataset) created among the multiple models trained using PCR were 0.982 and 0.9741. In terms of error, R2 or correlation coefficient, the PCA dataset performed better than the PCA+FS dataset. The second principal component had the highest explained variance in OLS (86.16%) and the highest coefficient of determination (R2) using OLS was obtained for the PCA dataset viz. 0.980. It is concluded that both the algorithms tested in this study were satisfactorily trained in their prediction of the body weights with OLS performing better than PCA in terms of R2 value.


https://arccarticles.s3.amazonaws.com/OnlinePublish/Final-article-attachemnt-with-doi-B-4831-6089603e9087340b9bdcbbb1.pdf

Artificial Intelligence Techniques for the Prediction of Body Weights in Sheep

A study was conducted through systematic survey of 25 villages of the four districts of Punjab, viz. Gurdaspur, Amritsar, Ferozpur and Ludhiana, for phenotypic characterization of Beetal goats. During the survey 1726 goats of different ages and sexes were studied for physical traits and 66 flocks were studied to know the m.anagement practices followed by the goat farmers. The physical traits, viz. colour of coat, retina, hooves and hom, orientation of hom and ear, structure of head, tail pattern and shape of udder and teats were recorded for the animals of both the sexes belonging to 3 age groups. The physical characters of the breed included predominately black or brown coat colour with white spots of different sizes, roman nose but not as prominent as in Jamunapari breed. Nine different body measurements were also recorded on the animal belonging to different age groups and sexes. The management practices were studied on different aspects of housing, breeding, feeding. Since the goat owners belong to poor farnler community and there is shrinkage in grazing land in the breeding tract of Beetal goats, the breed is declining in~number very fast. Hence immediate steps should be taken up to save this breed in its natural habitat.

Morphological studies and management of Beetal goats in its native tract

Background: Sheep farm data is often biased by extreme values which are generally introduced due to errors in manual measurement. These values interfere with the accuracy of estimations especially in state-of-the-art techniques like Machine Learning. Methods: Therefore, winsorization technique was attempted for the removal of outliers from sheep farm data data for 11 years (2011-2021) for body weights at different ages. Some outliers were deliberately introduced into the data to check the efficiency of the technique. This study was conducted during the year 2021.
Result: Our results indicate that outlier values of 15.3, 42, 44, 60, 90 for birth weight, weaning weight, 6-month, 9 month and 12-month body weight which were far from the normal range were removed using this technique. The mean and standard deviation values were altered after winsorization. Winsorization technique works well for sheep farm data to remove the bias introduced by outliers and also removes, to a large extent, the need for manual outlier removal in data.


https://arccarticles.s3.amazonaws.com/PublishedArticle/Final-attachment-published-BKAP397-6089603e9087340b9bdcbbb1.pdf

Outlier Removal in Sheep Farm Datasets Using Winsorization

Background: Kashmiri (Kashir) goat is nondescript and highly variable, medium sized, goat genetic resource of Kashmir. The goat possessing excellent survivability and is distributed in Kashmir valley. In Jammu and Kashmir, Gujjars and Bakerwals have adopted the sheep and goat rearing as their primary occupation. Body weights at different ages of life cycle of animals are important traits owing to their direct relation with farmers income.
Methods: Data on body weight and body measurements viz ear length (EL), body length (BL), body height at wither (BH), chest girth (CG) and tail length (TL) of 162 adult Kasmiri (Kashir) nondescript goats, irrespective of gender, were collected from Febuary 2019 to January 2020 to estimate the body weight from body measurements. The data were subjected to standard statistical analysis using SPSS software. Descriptive statistics and Pearson correlation was worked out to observe association among traits under study. The linear regression was utilized to predict body weight from linear body measurements.
Result: The average body weight (BW), ear length (EL), body length (BL), body height at wither (BH), chest girth (CG) and tail length (TL) of 36.94±0.99 kg, 13.13±0.36 cm, 55.48±0.43 cm, 65.93±0.62 cm, 69.61±0.73 cm and 12.94±0.15 cm, respectively were observed for Kashmiri goat in the present study. The body weight was having positive and significant correlations with BL, HW, CG and TL. The correlation of body weight with EL and TL was low. Similarly the correlation of body weight with HW was moderate whereas with BL and CG was significantly high indicating these two traits can define body weight of animal more accurately. Based on R2 (coefficient of determination) criteria, the best regression model for predicting body weight of Kashmiri goat was obtained by using combination of BL (body length) and CG (chest girth) with R2 (coefficient of determination) of 0.795. Prediction equation with R2 value of 0.806 was obtained when all traits were included in regression model where as prediction equation with R2 value of 0.589 was obtained when only chest girth was used as independent variable.


https://arccarticles.s3.amazonaws.com/PublishedArticle/Final-attachment-published-BKAP490-6089603e9087340b9bdcbbb1.pdf

Prediction of Body Weight from Linear Body Measurements in Kashmiri (Kashir) Goat

Exploring genetic diversity in cashmere goats of Ladakh for enhancing production

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