Abstract

The potential of micro plasma impacts in the growth of organic vegetables has been very difficult to predict. With the advent of deep learning methods, scientists are now able to develop predictive models that can accurately assess the effects of these impacts. Deep learning algorithms can be used to analyze the various environmental factors that influence the growth of organic vegetables, including temperature, humidity, sunlight, and soil type. With these inputs, the deep learning algorithms can learn complex relationships between these elements and the output of the growth of organic vegetables. By considering the interactions between environment and micro plasma impacts, the deep learning algorithms can make accurate predictions regarding the effects of these impacts on organic vegetable yields. The algorithm can be optimized to accurately predict the effect of these impacts in the future, allowing farmers to better plan for their crops. In addition, the deep learning algorithms can be used to analyze the effects of various factors on micro plasma impacts in organic vegetables. For example, the algorithm can analyze the effects of different combinations of fertilizer, water, and chemical inputs on the micro plasma impacts, allowing farmers to find the optimal crop growth conditions more quickly. The use of deep learning to predict the effects of micro plasma impacts on organic vegetable growth has the potential to improve crop yields, leading to more efficient agriculture practices.

Authors

J. Jasper Gnana Chandran¹, L. Sahaya Senthamil², C. Thamilarasi³, J. Jaganpradeep⁴
St. Mother Theresa Engineering College, India¹, PSNA College of Engineering and Technology, India², P.S.V College of Engineering and Technology, India³, SSM College of Engineering, India ⁴

Keywords

Micro Plasma, Organic, Vegetables, Deep Learning, Humidity, Temperature, Sunlight