Machine Learning Algorithms in Agriculture – Latest News from Jammu and Kashmir | Tourism

Dr. M. Iqbal Jeelani
Machine learning (ML) algorithms have emerged as promising alternative and complementary tools to modeling approaches commonly used in agriculture and related sciences. ML algorithms have gained popularity in agricultural production research, yield forecasting, and forest management. Machine learning is an application of artificial intelligence that allows a system to learn from examples and experiments without explicit programming. Machine learning includes a category of algorithms that allow software applications to become more accurate in predicting the outcomes of systems of interest for research. The basic premise of ML is to create algorithms that can receive input data and use statistical analysis to predict an output upon update. released as new data becomes available.
Creating algorithms that can take input data and use statistical analysis to predict an output while updating the outputs as new data becomes available is the fundamental principle of machine learning. Extracting more knowledge and selecting or recognizing patterns from large data sets are two aspects of machine learning and are mainly used to handle complex problems when human expertise fails because they can be continuously refined with greater precision. The emerging concept of machine learning, coupled with big data technologies and high-performance computing, has created new opportunities to quantify and understand data-intensive processes in new era smart agriculture. Today, one-day machine learning spans the entire field of agriculture throughout the growing and harvesting cycle, which begins with land preparation, seed selection, and feed measurement. in water? and eventually ends up with robots picking up the crop and determining readiness using computer vision. Machine learning can benefit agriculture at every stage, including soil management, crop management, disease detection, livestock management, and more.
Machine learning algorithms study evaporation processes, soil moisture and temperature to understand ecosystem dynamics and impact on agriculture. Now, machine learning-based daily applications are used to assess daily, weekly, or monthly evapotranspiration, enabling more efficient use of irrigation systems and predicting daily dew point temperature, which helps to identify expected weather phenomena. State-of-the-art machine learning algorithms have incorporated computer vision technologies to provide data for widespread multidimensional analysis of crops, weather and economics. Apart from that, machine learning plays a very important role in weed detection, which is a serious concern in traditional agricultural production. Weed detection is a very difficult task because it is very difficult to detect and differentiate them from the main crop. Such challenges can be overcome by applying low-cost ML algorithms without environmental issues. Algorithms such as artificial neural networks, support vector machines, decision trees, random forests, etc. that are used in crop management processes, which are still at the beginning of their journey, have already evolved into artificial intelligence systems. ML algorithms focus on the predictive accuracy of models rather based on modeling data with little or no human intervention, and can provide better decision support.
Uncertainty plays a fundamental role in all machine learning. Many aspects of it depend crucially on a careful probabilistic representation of uncertainty. One way to effectively manage uncertainty is to develop probabilistic ML algorithms that can provide a framework for representing and manipulating uncertainty in data, models, and predictions. Probabilistic ML algorithms and artificial intelligence are a very dynamic area of ​​research with widespread impacts beyond conventional pattern recognition problems in agricultural production. He will continue to play a pivotal role in the development of ever more powerful ML systems for future application in the agricultural system.
(Contributions from Afshan Tabasum, Mansha Gul-both SKUAST-Jammu Researchers)
(The author works as an assistant professor SKUAST-Jammu)

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