Utilization of AI - reshaping the future of food safety, agriculture and food security - a critical review.

Artificial intelligence is an emerging technology which harbors a suite of mechanisms that have the potential to be leveraged for reaping value across multiple domains. Lately, there is an increased interest in embracing applications associated with Artificial Intelligence to positively contribute to food safety. These applications such as machine learning, computer vision, predictive analytics algorithms, sensor networks, robotic inspection systems, and supply chain optimization tools have been established to contribute to several domains of food safety such as early warning of outbreaks, risk prediction, detection and identification of food associated pathogens.

Artificial intelligence algorithm comparison and ranking for weight prediction in sheep.

In a rapidly transforming world, farm data is growing exponentially. Realizing the importance of this data, researchers are looking for new solutions to analyse this data and make farming predictions. Artificial Intelligence, with its capacity to handle big data is rapidly becoming popular.

Comparison of various models for the estimation of heritability and breeding values.

The exploration of additive genetic variance for the selection of animals is the central paradigm in quantitative genetics and it is important to use appropriate animal models considering important factors. This study compares various factor effects for heritability and breeding values estimations on data collected on the Corriedale. Overall, the heritability estimates were the highest for birthweight (BW).

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

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.

Comparison of artificial intelligence algorithms and their ranking for the prediction of genetic merit in sheep.

As the amount of data on farms grows, it is important to evaluate the potential of artificial intelligence for making farming predictions. Considering all this, this study was undertaken to evaluate various machine learning (ML) algorithms using 52-year data for sheep. Data preparation was done before analysis.

Factors affecting wool characteristics of sheep reared in Kashmir.

This study was conducted on 82,908 records of purebred and upgraded Kashmir Merino sheep to evaluate the performance of breed over the years. The data pertaining to fiber diameter (FD), staple length (SL), clean wool yield percent (CWY %), number of crimps/cm (NCPC), and medullation percent (MP) spread over a period of 15 years (2013-2017) was collected from Fleece Testing Laboratory Nowshera, Srinagar. The highest CV (%) was observed for MP, whereas the lowest CV (%) was observed for FD (2.