Health Status Classification for Cows Using Machine Learning and Data Management on AWS Cloud

Abstract

The health and welfare of livestock are significant for ensuring the sustainability and profitability of the agricultural industry. Addressing efficient ways to monitor and report the health status of individual cows is critical to prevent outbreaks and maintain herd productivity. The purpose of the study is to develop a machine learning (ML) model to classify the health status of milk cows into three categories. In this research, data are collected from existing non-invasive IoT devices and tools in a dairy farm, monitoring the micro- and macroenvironment of the cow in combination with particular information on age, days in milk, lactation, and more. A workflow of various data-processing methods is systematized and presented to create a complete, efficient, and reusable roadmap for data processing, modeling, and real-world integration. Following the proposed workflow, the data were treated, and five different ML algorithms were trained and tested to select the most descriptive one to monitor the health status of individual cows. The highest result for health status assessment is obtained by random forest classifier (RFC) with an accuracy of 0.959, recall of 0.954, and precision of 0.97. To increase the security, speed, and reliability of the work process, a cloud architecture of services is presented to integrate the trained model as an additional functionality in the Amazon Web Services (AWS) environment. The classification results of the ML model are visualized in a newly created interface in the client application.

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

“…The pre-harvest stage involves preparatory activities such as land preparation, planting, and crop maintenance before harvesting. While there are fewer studies focused explicitly on the pre-harvest stage [78,[147][148][149][150] compared to the growth and post-harvest stages, several key areas warrant attention.…”

“…Deficient AI/ML models may inadvertently extend computation times, as expounded in [78,219]. Prolonged computations persist until yielding satisfactory outcomes.…”

“…SVR is a machine learning technique tailored for predicting continuous values by identifying a hyperplane that minimizes the margin between predicted and actual values, accommodating some error. The hyperplane is a linear function of input features that minimizes the distance between itself and predicted values [78,86]. XGBoost (XGB) XGBoost, an ensemble algorithm, employs gradient-boosting decision trees to sequentially train individual trees, each correcting the errors of the previous one.The model aggregates their classifications for a final prediction.…”

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

“…Several examples of applications of machine learning in precision agriculture [51] are reported, i.e., soil properties detection [52][53][54], crop yield predictions [55][56][57][58][59], disease [60][61][62][63] and weed detection [64][65][66], site-specific irrigation [67][68][69], and livestock production and management [70][71][72]. One of the most in-depth topics is the analysis of plant health with hyperspectral data [73].…”

Exploratory Data Analysis (EDA) untuk Mengetahui Distribusi Data Kualitas Susu Sapi

“…Kesehatan dan kesejahteraan hewan sangat penting bagi industri peternakan. Kesehatan dan kesejahteraan hewan pemasok makanan sangat tergantung pada bagaimana mereka dikelola oleh manusia [1]. Susu merupakan salah satu Minuman ini menawarkan banyak manfaat karena kaya akan kandungan nutrisi penting, termasuk berbagai vitamin, protein, kalsium, dan magnesium [2].…”