Evaluations of soil resistivity in relation to basal stem rot incidences using soil moisture sensor

• Main Authors: Abdul Aziz, Mohd Hamim, Bejo, Siti Khairunniza, Hashim, Fazirulhisyam, Ramli, Nur Hidayah, Ahmad, Desa
• Format: Article
• Language: English
• Published: Universiti Putra Malaysia Press 2019
• Online Access: http://psasir.upm.edu.my/id/eprint/69710/1/19%20JST-S0515-2019.pdf; http://psasir.upm.edu.my/id/eprint/69710/; http://www.pertanika.upm.edu.my/Pertanika%20PAPERS/JST%20Vol.%2027%20(S1).%202019/19%20JST-S0515-2019.pdf

Basal stem rot (BSR) caused by Ganoderma boninense is a major disease attacking the oil palm plantation in Malaysia, and incur big losses in palm oil industries. The disease is spread mostly by root either through spore availability in soil or roots contacts. Soil properties were reported to have significant influence on the growth of fungi. Meanwhile, the value of soil resistivity is influenced by soil properties. This paper presents a new approach of BSR detection by using soil moisture sensor which measures resistivity of soil in unit ohm (Ω) at 15 cm surrounding the basal stem of oil palm trees. The study was conducted on 39 oil palm trees at different healthiness levels. The sensor was embedded approximately 4.7 cm deep in the soil at eight different points for each palm. The results showed that healthy oil palm trees significantly have higher mean (ERMEAN ≥ 400) of electrical resistance (ER) readings compared to infected trees (ERMEAN< 400). More specifically, ER readings at points without symptoms (i.e. fruiting bodies and/or hollow) were significantly higher compared with ER readings at points where symptoms appeared even though the points of measurements were on the same palm. This finding has brought to the introduction of a new index to detect Ganoderma infection, named as K-index. Combination of ERMEAN taken from eight points of measurement and its K-index gave better results of detection and a new model was developed based on these two parameters (i.e. ERMEAN and K-index). The developed model has accuracy rates of 82% and gained 100% successful rate during validation. This research showed that soil resistivity can contribute to Ganoderma-infected detection in oil palms with a high degree of accuracy.