A Rapid Technique in Evaluating Tree Health Using Lidar Sensors

Abstract

Crop management is one of the key aspects in precision agriculture. On-site crop management includes a scheduled prediction and prescription of fertilization and pesticide application on specific areas of the field. A prediction of the tree health is much needed in order to decide a suitable prescription for the plant.  An autonomous vehicle, equipped with at least one LiDAR (light detection and ranging) sensor, could be used not only for detecting and mapping its surrounding, but to also help evaluate tree health for early distress detection. Currently, farmers have to rely on eyesight to identify trees or plants in distress. Big-scale plantations depend on costly scheduled airborne monitoring routines, which also relies on human vision by scouring through hours of aerial video footage. Both techniques have similar weaknesses in terms of the time it takes to detect a tree in distress and the accuracy of the detection using human vision. The objective of this research is to propose a technique in evaluating tree health using a simple LiDAR sensor that is commonly used in autonomous vehicles. In order to achieve this objective, an evaluation of the different intensity characteristics of tree leaves versus fruits was carried out, both in the lab and in the field. This study has chosen oil palm trees as its subject, as the problem of health monitoring in oil palm plantations is evident. A LiDAR with a 905 nm near-infrared (NIR) laser is used to scan both individual healthy leaves in the lab and different oil palm trees in the field. Since a LiDAR sensor is normally used for ranging, a systematic process was proposed to capture the reflected intensity value of the laser beam that was transmitted to the object. This whole system can be realized using a LiDAR sensor, servo motors, and an Arduino board. Processing software was used to test and store the captured information from the sensor. Later, MATLAB was used to plot the intensity map of the leaves and oil palm tree, classify the range intensity into histograms, and calculate the leaf area index (LAI) for the oil palm trees. From the experimental results, it is found that the reflectance intensity of the leaves shows consistent range values between 155-160 magnitude both in the lab and in the field. From there, three different trees with different number of leaves were scanned and evaluated based on their LAI values. The health of the tree is then predicted, where a healthy tree is estimated to have a higher LAI value. The resulting LAI value is found to correlate with the evaluation using eyesight. This proves that although using a single-wavelength NIR laser beam provided by the LiDAR sensor, as compared to multiple wavelengths of a spectrometer, the difference between the oil palm leaves, fruits and the background noise could be determined. In the future, where multi-wavelength laser LiDAR sensors could be possible, more materials could be characterized. In conclusion, detection, mapping, and materials characterization could be done by an autonomous vehicle utilizing a LiDAR sensor, where tree health could be predicted for crop care management.