Low cost sensor based embedded system for plant protection and pest control

Prediction depicts the way the things will happen in the future, but not always based on experience or knowledge. Prediction is helpful in various fields and it brings out together the past and current data as a basis to develop reasonable expectations regarding the future. The main objective of this work is to predict the occurrence of risk factor in apple caused by apple scab in Himalayan regions. Firstly, we obtained the readings of the necessary environmental parameters like temperature, humidity and leaf wetness duration, which leads to the growth of disease and pests by interfacing sensors with the Raspberry Pi board and calculation of the infection index of the disease of apple. As a prediction model, the Beta regression model was used as a standard equation from which the severity index was derived and then in the prediction subsystem, the Python programming language was used to predict the severity of apple scab disease to apple caused by ascomycete fungus, Venturia inaequalis. Using Python and by analyzing pest surveillance data set of apple scab, we developed a model for the prediction of pests. Further, we usedthe MySQL database connectivity, to send the data and the required outputs to the server where the authorized officials could access the data. The result showed that Raspberry Pi and Python successfully predicted the pest attack in advance. In this way it is a novel, easy to handle, economical system for the apple growers and farmers. The prediction is done in present day as well as seven days in advance and the information is disseminatedto farmers to take the decision of pesticide spray, so the objective is to reduce the pesticide spray.