As nearly half of the people in the world live on rice, so the rice leaf disease detection is very important for our agricultural sector. Many researchers worked on this problem and they achieved different results acc...As nearly half of the people in the world live on rice, so the rice leaf disease detection is very important for our agricultural sector. Many researchers worked on this problem and they achieved different results according to their applied techniques. In this paper, we applied AlexNet technique to detect the three prevalence rice leaf diseases termed as bacterial blight, brown spot as well as leaf smut and got a remarkable outcome rather than the previous works. AlexNet is a special type of classification technique of deep learning. This paper shows more than 99% accuracy due to adjusting an efficient technique and image augmentation.展开更多
The analysis of environmental daily evaporation plays a vital role in the field of agriculture. It is very essential to know the daily evaporation rate of a particular area for proper cultivation. So, we need a standa...The analysis of environmental daily evaporation plays a vital role in the field of agriculture. It is very essential to know the daily evaporation rate of a particular area for proper cultivation. So, we need a standard prediction model which can predict the daily evaporation. In this paper, we use subtractive clustering and Fuzzy logic to predict daily evaporation of a particular area. The input data used in the paper are: maximum soil temperature, average soil temperature, average air temperature, minimum relative humidity, average relative humidity and total wind, which are related to the daily evaporation of a particular area as the output. The accuracy of output of the paper is compared with the previous model of Artificial Neural Network (ANN) and we get better result towards the target value. The finding of the paper is applicable in environmental science, geological science and agriculture.展开更多
文摘As nearly half of the people in the world live on rice, so the rice leaf disease detection is very important for our agricultural sector. Many researchers worked on this problem and they achieved different results according to their applied techniques. In this paper, we applied AlexNet technique to detect the three prevalence rice leaf diseases termed as bacterial blight, brown spot as well as leaf smut and got a remarkable outcome rather than the previous works. AlexNet is a special type of classification technique of deep learning. This paper shows more than 99% accuracy due to adjusting an efficient technique and image augmentation.
文摘The analysis of environmental daily evaporation plays a vital role in the field of agriculture. It is very essential to know the daily evaporation rate of a particular area for proper cultivation. So, we need a standard prediction model which can predict the daily evaporation. In this paper, we use subtractive clustering and Fuzzy logic to predict daily evaporation of a particular area. The input data used in the paper are: maximum soil temperature, average soil temperature, average air temperature, minimum relative humidity, average relative humidity and total wind, which are related to the daily evaporation of a particular area as the output. The accuracy of output of the paper is compared with the previous model of Artificial Neural Network (ANN) and we get better result towards the target value. The finding of the paper is applicable in environmental science, geological science and agriculture.
