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@article{169246,
        author = {SURAJIT DEY},
        title = {Enhancing Precision Agriculture with Machine Learning Innovations},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {883-892},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169246},
        abstract = {Agriculture plays a vital role in the economic growth of any country. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to fulfil the food requirement of the present population. Smart agriculture comprises a set of technologies that combines sensors, information systems, enhanced machinery, and informed management to optimize production by accounting for variability and uncertainties within agricultural systems. Smart farming has emerged as an innovative tool to address current challenges in agricultural sustainability. The method that drives this cutting-edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed.ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. Increasing environmental consciousness of the general public is necessitating us to modify agricultural management practices for sustainable conservation of natural resources such as water, air and soil quality, while staying economically profitable. In this article, author presents a systematic review of ML applications in the field of agriculture. The areas that are focused are prediction of soil parameters such as organic carbon and moisture content, crop yield prediction, disease in crops and species detection. ML with computer vision is reviewed for the classification of a different set of crop images in order to monitor the crop quality and yield assessment. This approach can be integrated for enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behavior based on ML models using data collected by collar sensors, etc. Smart irrigation which includes drip irrigation and intelligent harvesting techniques are also reviewed that reduces human labor to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.},
        keywords = {Disaster Management, Flood Prediction, Classification, Machine Learning.},
        month = {November},
        }