A multi-layer perceptron-based approach for early detection of BSR disease in oil palm trees using hyperspectral images

Basal Stem Rot (BSR) disease caused by Ganoderma boninense is identified as the biggest threat in oil palm industry in Malaysia, resulting in significant yield losses. Effective BSR disease detection is important for plantation management to ensure stable palm oil production. Existing method is done by experience personnel, via visual inspection it is very time consuming. Rapid development of unmanned aerial vehicle (UAV) and machine learning has the potential to address this issue with higher efficiency. This paper proposed a new framework to automate BSR disease detection with UAV images to improve time efficiency and automate detection process. The proposed method has two steps, first hyperspectral image (HSI) pre-processing, followed by artificial neural network disease detection. Multilayer-Perceptron model is introduced to learn spectral features from different infection stages. The model is trained with ground truth collected by trained surveyors. The HSI sample size consists of 2 healthy trees, 5 Stage A (mild infection), 5 Stage B (moderate infection), and 3 Stage C (severe infection). Performance is examined with support vector machine (SVM), 1 dimensional convolutional network (1D CNN), and several vegetation indices, namely Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge (NDRE), Optimised Soil-Adjusted Vegetation Index (OSAVI), and Merris Terrestrial Chlorophyll Index (MTCI). All machine learning algorithms can segregate infection stages, MLP modal had a highest overall accuracy 86.67%, compared to SVM and 1D CNN at 66.67% and 73.33%. Whereas for vegetation index, it can only detect Stage C tree, and not able to differentiate between Healthy, Stage A and Stage B tree. In term of computational cost, MLP modal had balance performance with moderate training time, but faster inference time. It demonstrates effectiveness on BSR disease detection, even at early infection stage.