A Study on Farm Financial Statement Analysis using Machine Learning and Statistical Techniques

Farm financial statements capture the daily transaction data in farming, yet many farmers primarily concentrate on field preparation, sowing seeds, irrigation, and harvesting due to their expertise in these areas. However, aspects such as identifying cost factors, understanding costs, and assessing profitability often receive less attention. Unpredictable events like the pandemic, droughts, and heavy rainfall have led to significant crop losses, exacerbating the challenges faced by farmers. Successful farmers not only excel in agricultural activities but also diligently work on financial statements to identify areas for improvement. This article provides an overview of various methods researchers have employed to identify and analyze costs in farming. It highlights the use of statistical analysis, machine learning, and deep learning in this domain. The review examines the procedures of selected research studies, discussing their benefits and limitations to provide a comprehensive understanding of the techniques used for cost estimation and analysis in agriculture. By focusing on these advanced methodologies, farmers can better manage financial risks and enhance their overall profitability despite the challenges posed by external factors.