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Soh, A. C.; Mayes, Sean; Roberts, Jeremy A.; Rajanaidu, N.; Din, Amiruddin Mohd; Marhalil, Marjuni; Norziha, Abdullah; Meilina, Ong-Abdullah; Fadila, Ahmad Malike; Azwani, Abu Bakar Nor; Anak, Libin Adelinna; Yaakub, Zulkifli; Mustafa, Suzana; Ithnin, Maizura Binti; Din, Ahmad; Barcelos, Edson; Amblard, Philippe

doi:10.1201/9781315119724-3

Cited by 1 publication

(2 citation statements)

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“…From the literature, it was also found that some prominent research and development (R&D) organizations in the world like CENIPALMA (Columbia), NIFOR (Nigeria), CIRAD (France), IOPRI (Indonesia), and MPOB (Malaysia) deal with multiple aspects of oil palm plantation development in their countries. FELDA is another local government agency in Malaysia that encourages different organizations for agricultural research [112,113]. Current development in the global and Malaysian oil palm industry is a result of significant research combined with good management, a compatible climate, and well-established infrastructure [112].…”

Section: Results-based Discussionmentioning

confidence: 99%

“…FELDA is another local government agency in Malaysia that encourages different organizations for agricultural research [112,113]. Current development in the global and Malaysian oil palm industry is a result of significant research combined with good management, a compatible climate, and well-established infrastructure [112]. Given one full credit to each author, statistics revealed that Malaysia is ranked number 1 for publishing approximately 62% of the articles, followed by Indonesia with 18% of the articles of the reviewed material.…”

Section: Results-based Discussionmentioning

confidence: 99%

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Oil Palm and Machine Learning: Reviewing One Decade of Ideas, Innovations, Applications, and Gaps

et al. 2021

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Machine learning (ML) offers new technologies in the precision agriculture domain with its intelligent algorithms and strong computation. Oil palm is one of the rich crops that is also emerging with modern technologies to meet global sustainability standards. This article presents a comprehensive review of research dedicated to the application of ML in the oil palm agricultural industry over the last decade (2011–2020). A systematic review was structured to answer seven predefined research questions by analysing 61 papers after applying exclusion criteria. The works analysed were categorized into two main groups: (1) regression analysis used to predict fruit yield, harvest time, oil yield, and seasonal impacts and (2) classification techniques to classify trees, fruit, disease levels, canopy, and land. Based on defined research questions, investigation of the reviewed literature included yearly distribution and geographical distribution of articles, highly adopted algorithms, input data, used features, and model performance evaluation criteria. Detailed quantitative–qualitative investigations have revealed that ML is still underutilised for predictive analysis of oil palm. However, smart systems integrated with machine vision and artificial intelligence are evolving to reform oil palm agri-business. This article offers an opportunity to understand the significance of ML in the oil palm agricultural industry and provides a roadmap for future research in this domain.

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Section: Results-based Discussionmentioning

confidence: 99%

Section: Results-based Discussionmentioning

confidence: 99%