Future prospects and challenges of digital transformation in agriculture and dairy industries

This book, Smart Agriculture: Harnessing Machine Learning for Crop Management, is a comprehensive guide designed to explore the various facets of integrating machine learning into agricultural practices. It aims to provide readers with a solid foundation in machine learning concepts while demonstrating their practical applications in real-world farming scenarios. It also examines the role of remote monitoring and precision agriculture, highlighting how technologies such as remote sensing and recurrent neural networks can optimize farming practices.

This book:

• Emphasizes sustainable agricultural practices and data-driven decision-making for eco-friendly farming.
• Highlights the importance of using environmentally friendly practices, and how machine learning can play a pivotal role in achieving sustainability goals.
• Discusses topics such as crop optimization, disease detection, pest control, resource management, precision agriculture, and sustainability.
• Covers predictive analytics for weather forecasting, Internet of Things applications for precision agriculture, and the role of sensors in data collection.
• Illustrates optimizing resource allocation, irrigation with artificial intelligence, and machine learning for soil health assessment.

Whether you are a researcher, a student, an agricultural professional, or a technology enthusiast, this book offers valuable insights into the transformative power of machine learning in agriculture. It invites readers to explore the potential of machine learning to transform farming practices, improve food security, and promote environmental sustainability.

Book Chapter that I wrote:

This multidisciplinary study uses time-series prediction, modeling for prediction, and ecological effect assessments to navigate the challenging landscapes of dairy and the agricultural sector. Crop yields are predicted by predictive models using random forests along with support vector machines, both of which are based on soil-related indicators and climate data. The results show that random forests have been higher-ups, achieving an mean-squared error (MSE) of 0.012 along with a correlation coefficient of 0.89, demonstrating strong predictive abilities. In parallel, the dairy industry uses long short-term memory (LSTM) connections to accurately predict trends in milk production. Through an accuracy of about 93.8%, along with a mean absolute percentage error (MAPE) of 6.2%, the LSTM model exceeds baselines, demonstrating its effectiveness. When comparing protein extracted from plants against conventional dairy customs, evaluations of the environmental impact show that the greenhouse gas emissions of the latter are significantly higher (9.5 kgCO2e/kg) than that of the former (2.3 kgCO2e/kg). The investigation integrates information on buyer attitudes in dairy product pricing, agricultural solar energy for sustainable cultivation, expandable control of knowledge, automation’s consequences for farm operation in the face of changing climates, and developments in agricultural automated machinery, drawing upon the most recent scholarship. The research highlights the effects of farming methods on the surroundings in addition to offering a thorough understanding of upcoming developments and difficulties in dairy, along with agriculture.