Crop Yield Prediction In SmartFarm Agriculture System for Farmers Using IoT

Abstract

Agriculture is the most consequence application area for developing countries like India. India is a cultivated country and about 70% of the population depends on agriculture. Smart agriculture which moved the growth from statistical to quantitative approaches. To accumulate productivity of agriculture its demands new technology. State-of-the-art (IoT) based agriculture platforms used in agriculture to become a smart agriculture.  A major problem that can be overcome based on past experience is the problem of yield estimation. Therefore, a brief study of crop yield prediction is proposed using CNN methodology and IoT technology.  Identification of the plant diseases is the key to prevent the losses in the yield and quantity of the agriculture product. Hence image processing using Convolution neural network (CNN) is used for the detection of plant diseases. Google API is used to access crop production patterns in response to climatic conditions such as rainfall, temperature, relative humidity, evaporation and sunshine etc. with specific region. Proposed system designed two modules , Crop prediction is a pre-condition and prediction of disease is a post-condition for the collection of data from a field. This technology helping the growers throughout the crop stages, from sowing until harvesting is explained.  It lets farmers to improve quality in decision making and recommend fertilizers for probable disease control with their shop.