Crop Yield Prediction Using Image Processing

Abstract

Agriculture is a major Indian occupation. It Plays a vital role contributing to over 18% of India's GDP and provides jobs to 50% of the population of India Population growth is a major food security challenge. Population growth is increasing the need for farmers to produce more in the same agricultural country in terms of increasing supply. Computing the yield of any fruit or a vegetable takes a lot of computation time and requires too much of processing work. Technology can help farmers to produce more with the help of forecasting. The proposed model assists farmers in predicting crop yields. Fruit images are obtained with integrated charging devices (CCD) and the background of each image will be removed using a different algorithm to separate the fruit area in the input image. Fruits are calculated based on the centroid of the fruit. A machine-based approach is used to filter fruit by predicting the degree of maturity and aims to change the staffing system. The program includes pre-image processing, feature extraction and fruit classification using novel computational techniques such Machine Learning, Artificial intelligent, deep learning and so on. Using K means the highest accuracy of the fruit number algorithm is available. The project presents a computerized concept and techniques for machine learning of tree fruit acquisition, counting and sorting. With the introduction of IoT to use image processing the project assists farmers in predicting the yield. The findings can be stored in a database that enables the farmer to move whenever needed and keep track of the yield.