Palm leaf nutrient deficiency detection using convolutional neural network (CNN)

Palm oil, scientifically known as Elaeis guineensis, is a rapidly growing commercial sector in Southeast Asia with a diverse economic composition. Palm oil plantations are crucial in economic activities and growth, as they generate employment in managing the palm oil quality. However, the lack of nutrients can affect the growth and quality of the crops. The manual detection of palm leaf nutrient deficiency can be one of the challenges as the visual symptoms of the deficiency demonstrate a similar representation. Thus, in this study, the palm leaf nutrient deficiency detection using Convolutional Neural Network (CNN) is proposed. CNN or ConvNet is a branch of deep neural networks in Deep Learning that is commonly used in analysing images and has proven to produce better feature extraction from dataset. A total of 350 images of healthy leaf and six types of palm leaf nutrient deficiency are Nitrogen, Potassium, Magnesium, Boron, Zinc, and Manganese were tested. The application of CNN to a variety of testing datasets returned good detection accuracy at 94.29 %. It can be deduced that the proposed implementation of CNN for palm leaf nutrient deficiency detection is found to be successful. Nonetheless, the number of datasets could be increased in the future to improve the detection performance.