Unmanned Aerial Vehicle (UAV)-Based Remote Sensing for Early-Stage Detection of Ganoderma

Abstract

Early detection of Basal Stem Rot (BSR) disease in oil palms is an important plantation management activity in Southeast Asia. Practical approaches for the best strategic approach toward the treatment of this disease that originated from Ganoderma Boninense require information about the status of infection. In spite of the availability of conventional methods to detect this disease, they are difficult to be used in plantation areas that are commonly large in terms of planting hectarage; therefore, there is an interest for a quick and delicate technique to facilitate the detection and monitoring of Ganoderma in its early stage. The main goal of this paper is to evaluate the use of remote sensing technique for the rapid detection of Ganoderma-infected oil palms using Unmanned Aerial Vehicle (UAV) imagery integrated with an Artificial Neural Network (ANN) model. Principally, we sought for the most representative mean and standard deviation values from green, red, and near-infrared bands, as well as the best palm circle radius, threshold limit, and the number of hidden neurons for different Ganoderma severity levels. With the obtained modified infrared UAV images at 0.026 m spatial resolution, early BSR infected oil palms were most satisfactorily detected with mean and standard deviation derived from a circle radius of 35 pixels of band green and near-infrared, 1/8 threshold limit, and ANN network by 219 hidden neurons, where the total classification accuracies achieved for training and testing the dataset were 97.52% and 72.73%, respectively. The results from this study signified the utilization of an affordable digital camera and UAV platforms in oil palm plantation, predominantly in disease management. The UAV images integrated with the Levenberg–Marquardt training algorithm illustrated its great potential as an aerial surveillance tool to detect early Ganoderma-infected oil palms in vast plantation areas in a rapid and inexpensive manner.

Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods

“… Ahmadi et al (2022) further studied on the use of an UAV together with ANN model to analyze and detect early stage BSR infection in an oil palm tree. UAV that Ahmadi et al (2022) used was a Hexacopter Tarot 680PRO folding vehicle TL68P00.…”

“… Ahmadi et al (2022) further studied on the use of an UAV together with ANN model to analyze and detect early stage BSR infection in an oil palm tree. UAV that Ahmadi et al (2022) used was a Hexacopter Tarot 680PRO folding vehicle TL68P00. The image collection was done using Canon Powershot SX260 HS digital camera that was NIR-modified using an external NIR filter.…”

“…For corresponding classes, the accuracy dropped to 75% for healthy oil palm and 57.14% for early infected oil palm. This scenario was due to half of the early infected oil palm were misclassified as healthy oil palm since early infected oil palm shows little differences at areas such as chlorophyll content at early stages of infection ( Ahmadi et al, 2022 ).…”

Tree Stem Detection and Crown Delineation in a Structurally Diverse Deciduous Forest Combining Leaf-On and Leaf-Off UAV-SfM Data

“…Deep learning methods: Deep learning methods, such as instance segmentation, are increasingly being utilized for the detection and delineation of single tree crowns [34]. Commonly used model architectures are Mask R-CNN [35], artificial neural networks [15], or U-nets [36]. These methods offer advantages such as the ability to use multi-band images as input, instead of relying solely on a single band, and a focus on textural features, which is beneficial as adjacent trees might have very similar spectral properties.…”

“…Operating at low flight altitudes, they provide images with very high geometrical resolution of a few centimeters, enabling analysis on individual tree level and even capturing within-variability of single tree crowns [12]. UAV data have, therefore, been used to derive a range of forest parameters quantifying the vertical and horizontal forest structure-such as tree height, breast height diameter, crown shapes, and canopy gaps-categorizing tree species, estimating tree health and above-ground biomass, or detecting diseases [13][14][15].…”