Mob-based cattle weight gain forecasting using ML models

Forecasting mob based cattle weight gain (MB CWG) may benefit large livestock farms, allowing farmers to refine their feeding strategies, make educated breeding choices, and reduce risks linked to climate variability and market fluctuations. In this paper, a novel technique termed MB CWG is proposed to forecast the one month advanced weight gain of herd based cattle using historical data collected from the Charles Sturt University Farm. This research employs a Random Forest (RF) model, comparing its performance against Support Vector Regression (SVR) and Long Short Term Memory (LSTM) models for monthly weight gain prediction. Four datasets were used to evaluate the performance of models, using 756 sample data from 108 herd-based cattle, along with weather data (rainfall and temperature) influencing CWG. The RF model performs better than the SVR and LSTM models across all datasets, achieving an R^2 of 0.973, RMSE of 0.040, and MAE of 0.033 when both weather and age factors were included. The results indicate that including both weather and age factors significantly improves the accuracy of weight gain predictions, with the RF model outperforming the SVR and LSTM models in all scenarios. These findings demonstrate the potential of RF as a robust tool for forecasting cattle weight gain in variable conditions, highlighting the influence of age and climatic factors on herd based weight trends. This study has also developed an innovative automated pre processing tool to generate a benchmark dataset for MB CWG predictive models. The tool is publicly available on GitHub and can assist in preparing datasets for current and future analytical research..

Learning-based estimation of cattle weight gain and its influencing factors

Many cattle farmers still depend on manual methods to measure the live weight gain of cattle at set intervals, which is time consuming, labour intensive, and stressful for both the animals and handlers. A remote and autonomous monitoring system using machine learning (ML) or deep learning (DL) can provide a more efficient and less invasive method and also predictive capabilities for future cattle weight gain (CWG). This system allows continuous monitoring and estimation of individual cattle live weight gain, growth rates and weight fluctuations considering various factors like environmental conditions, genetic predispositions, feed availability, movement patterns and behaviour. Several researchers have explored the efficiency of estimating CWG using ML and DL algorithms. However, estimating CWG suffers from a lack of consistency in its application. Moreover, ML or DL can provide weight gain estimations based on several features that vary in existing research. Additionally, previous studies have encountered various data related challenges when estimating CWG. This paper presents a comprehensive investigation in estimating CWG using advanced ML techniques based on research articles (between 2004 and 2024). This study investigates the current tools, methods, and features used in CWG estimation, as well as their strengths and weaknesses. The findings highlight the significance of using advanced ML approaches in CWG estimation and its critical influence on factors. Furthermore, this study identifies potential research gaps and provides research direction on CWG prediction, which serves as a reference for future research in this area.

Machine Learning Techniques for Estimating Soil Moisture from Mobile Captured Images

Precise Soil Moisture (SM) assessment is essential in agriculture. By understanding the level of SM, we can improve yield irrigation scheduling which significantly impacts food production and other needs of the global population. The advancements in smartphone technologies and computer vision have demonstrated a non-destructive nature of soil properties, including SM. The study aims to analyze the existing Machine Learning (ML) techniques for estimating SM from soil images and understand the moisture accuracy using different smartphones and various sunlight conditions. Therefore, 629 images of 38 soil samples were taken from seven areas in Sydney, Australia, and split into four datasets based on the image-capturing devices used (iPhone 6s and iPhone 11 Pro) and the lighting circumstances (direct and indirect sunlight). A comparison between Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Convolutional Neural Network (CNN) was presented. MLR was performed with higher accuracy using holdout cross-validation, where the images were captured in indirect sunlight with the Mean Absolute Error (MAE) value of 0.35, Root Mean Square Error (RMSE) value of 0.15, and R^2 value of 0.60. Nevertheless, SVR was better with MAE, RMSE, and R^2 values of 0.05, 0.06, and 0.96 for 10-fold cross-validation and 0.22, 0.06, and 0.95 for leave-one-out cross-validation when images were captured in indirect sunlight. It demonstrates a smartphone camera's potential for predicting SM by utilizing ML. In the future, software developers can develop mobile applications based on the research findings for accurate, easy, and rapid SM estimation.