Nutrient deficiency detection in Maize (Zea mays L.) leaves using image processing

Abstract

Maize is one of the world's leading food supplies. Therefore, the crop's production must continue to reproduce to fulfill the market demand. Maize is an active feeder, therefore, it need to be adequately supplied with nutrients. The healthy plants will be in deep green color to indicate it consist of adequate nutrient. Current practice to identify the nutrient deficiency on maize leaf is throught a laboratory test. It is time consuming and required agriculture knowledge. Therefore, an image processing approach has been done to improve the laboratory test and eliminate a human error in identification process. The purpose of this research is to help agriculturist, farmers and researchers to identify the type of maize nutrient deficiency to determine an action to be taken. This research using image processing techniques to determine the type of nutrient deficiency that occurs on the plant leaf. A combination of Gray-Level Co-Occurrence Matrix (GLCM), hu-histogram and color histogram has been used as a parameter for further classification process. Random forest technique was used as classifiers manage to achive 78.35% of accuracy. It shows random forest is a suitable classifier for nutrient deficiency detection in maize leaves. More machine learning algorithm will be tested to increase current accuracy.