Citation :

Sheetal Sharma, 2023. "Automatic Damage Detection of Historic Masonry Buildings Based on Densenet Deep Learning Model" ESP International Journal of Advancements in Science & Technology (ESP-IJAST)  Volume 1, Issue 3: 20-26.

Abstract :

Agriculture is the backbone of human existence because it provides the bulk of the world's food supplies and a variety of other raw materials. It's a major factor in the growth and prosperity of any country. It also provides them with several commercial opportunities. Improvements to the national economy depend critically on progress in the agricultural sector. Planting is made more difficult in an uncontrolled environment by global warming. Farmers that practice sustainable agriculture rely on soil that is high in organic matter and naturally rich in minerals. It also needs a lot of space and water, and there are associated labor costs for plowing and weeding. When it comes to seasonal plants, the harvest falls short of both consumer demands and producers' hopes for increased output. As a result, farmers should seek out a technique that allows them to easily regulate environmental variables like temperature, humidity, and light intensity throughout the year while spending as little as possible. Hydroponic farming, in which plants are grown in a nutrient-rich solution rather than in soil and direct sunlight, is presented in this suggested study. In hydroponics, the roots of the plants are kept above ground and fed a solution of minerals and water. This technique is a form of indoor agriculture that is not affected by the weather and saves money by not necessitating plowing or other labor-intensive practices. Humidity, temperature, and water level are monitored and adjusted using a microcontroller Kit coupled to a wireless sensor network with internet. This Internet of Things technology would allow the authorized individual to check in on the plant's progress in real time, wherever they might be. In addition, LSTM, a deep learning model, is employed to anticipate potential outcomes.

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Keywords :

Deep Learning Model, Damage Detection, Masonry Buildings.