Abstract
There are various types of pathogens that occur in plants, due to the fact of climate changes, weather changes, seasons changes and the significance of environmental (temperature, humidity, rainfall, etc.) changes. The consequence of plant disease affects our agriculture industry and agriculture sector. It affects our plant growth, production growth, and economic growth throughout the world. So, to prevent the diseases, necessary to understand weather conditions and also identify corresponding environmental factors in plant diseases. Therefore, in this study, analysis of the different types of plant diseases and identification of corresponding environmental factors in plum data using the artificial neural network. Using neural network model to identify the environmental factors and the purpose of the correlation method is to find out the relationship between two variables (the actual value of diseases and the predicted value of diseases). Finally, in result explained detailed to identify the environmental factors in plum data.
Highlights
A disease is the disorder of structure or function in a human, animal, and plant [1]
To prevent plant disease, we need to know the condition of environmental factors that occur in plant diseases
S Chakraborty and et al, analysed potential impact on plant diseases for the reason of climate changes [5]
Summary
A disease is the disorder of structure or function in a human, animal, and plant [1]. The consequence of plant disease affects our agriculture industry and agriculture sector. In ANN model, the inputs are, the combination of environmental condition factors (temperature, humidity, rainfall, wind speed, etc.) and the produced outputs are plum plant diseases. To find the relationship between two variables, i.e. the actual value of plum diseases and the predicted value of plum diseases. The main purpose of the correlation is to checking the actual value of plum diseases and the predicted value of plum diseases. Based on this process, we identified the different combination of environmental factors. We arranged the data in table 1 and it shows the top 5 ranking order of environmental factors depends on descending error values
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