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

Abstract

Oil palm is a key agricultural resource in Malaysia. However, palm disease, most prominently basal stem rot caused at least RM 255 million of annual economic loss. Basal stem rot is caused by a fungus known as Ganoderma boninense. An infected tree shows few symptoms during early stage of infection, while potentially suffers an 80% lifetime yield loss and the tree may be dead within 2 years. Early detection of basal stem rot is crucial since disease control efforts can be done. Laboratory BSR detection methods are effective, but the methods have accuracy, biosafety, and cost concerns. This review article consists of scientific articles related to the oil palm tree disease, basal stem rot, Ganoderma Boninense, remote sensors and deep learning that are listed in the Web of Science since year 2012. About 110 scientific articles were found that is related to the index terms mentioned and 60 research articles were found to be related to the objective of this research thus included in this review article. From the review, it was found that the potential use of deep learning methods were rarely explored. Some research showed unsatisfactory results due to limitations on dataset. However, based on studies related to other plant diseases, deep learning in combination with data augmentation techniques showed great potentials, showing remarkable detection accuracy. Therefore, the feasibility of analyzing oil palm remote sensor data using deep learning models together with data augmentation techniques should be studied. On a commercial scale, deep learning used together with remote sensors and unmanned aerial vehicle technologies showed great potential in the detection of basal stem rot disease.

Analisis Penerapan Metode User-Centered Design pada Augmented Reality (AR) dengan Marker Based Tracking

“…Metode Marker based tracking yaitu metode tracking pada AR yang dalam pengoprasiannya menggunakan marker dari objek dua dimensi. Objek tersebut kemudian akan menjadi acuan yang akan dibaca oleh kamera dan dimasukkan kedalam komputer [11], [19]- [21]. Metode Markerless yaitu sebuah metode dalam AR yang tanpa mencetak marker atau penanda untuk menampilkan suatu objek digital yang sudah ditentukan.…”

An In-Depth Study of Phytopathogenic Ganoderma: Pathogenicity, Advanced Detection Techniques, Control Strategies, and Sustainable Management

“…Cultural practices, such as the use of disease-free planting materials, proper sanitation, and crop rotation, can help prevent the introduction and spread of the Ganoderma pathogen [ 18 , 26 ]. Chemical control methods, including the use of fungicides, can provide some control, but their long-term sustainability and environmental impact need careful consideration [ 27 , 28 ]. Biological control agents have emerged as promising alternatives for Ganoderma management [ 28 , 29 ].…”

Detection of Bagworm Infestation Area in Oil Palm Plantation Based on UAV Remote Sensing Using Machine Learning Approach

“…These techniques were used to detect pests and diseases in a variety of crops, including coffee [19], wheat [20], citrus [21], cotton [22] and forests [16,23], as summarized in Table 2. Despite the number of various remote sensing approaches that were used to monitor pests and diseases in oil palm plantations [24,25], their application at the UAV platform together with vegetation indices and machine learning techniques is still limited [26,27]. Based on Table 2, it also can be concluded that the same vegetation indices can be used to detect different types of pests and diseases in different types of crops.…”