Using Bayesian networks to predict future yield functions with data from commercial oil palm plantations: A proof of concept analysis

Chapman, R. A.; Cook, Simon; Donough, C. R.; Lim, Ya Li; Ho, Philip Vun Vui; Lo, Koon Wai; Oberthür, Thomas

doi:10.1016/j.compag.2018.06.006

Cited by 40 publications

(20 citation statements)

References 24 publications

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“…However, we suggest that schemes, such as those to collect the cacao data we have here with distinct management treatments superimposed on farmers fields 20 , can be used. Furthermore, even without superimposing management practices, simply monitoring crop performance, weather and the variation in management practices of farmers can be used to relate yield to variation in weather patterns and management 28 – 30 . However, this is only effective if the data of a large number of cropping events is brought together for analysis, which requires social organization and the willingness to share data 28 .…”

Section: Discussionmentioning

confidence: 99%

Crop response to El Niño-Southern Oscillation related weather variation to help farmers manage their crops

Chapman¹,

Cock

Samson

et al. 2021

Sci Rep

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Although weather is a major driver of crop yield, many farmers don’t know in advance how the weather will vary nor how their crops will respond. We hypothesized that where El Niño-Southern Oscillation (ENSO) drives weather patterns, and data on crop response to distinct management practices exists, it should be possible to map ENSO Oceanic Index (ENSO OI) patterns to crop management responses without precise weather data. Time series data on cacao farm yields in Sulawesi, Indonesia, with and without fertilizer, were used to provide proof-of-concept. A machine learning approach associated 75% of cacao yield variation with the ENSO patterns up to 8 and 24 months before harvest and predicted when fertilizer applications would be worthwhile. Thus, it’s possible to relate average cacao crop performance and management response directly to ENSO patterns without weather data provided: (1) site specific data exist on crop performance over time with distinct management practices; and (2) the weather patterns are driven by ENSO OI. We believe that the principles established here can readily be applied to other crops, particularly when there’s little data available on crop responses to management and weather. However, specific models will be required for each crop and every recommendation domain.

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Section: Discussionmentioning

confidence: 99%

Crop response to El Niño-Southern Oscillation related weather variation to help farmers manage their crops

Chapman¹,

Cock

Samson

et al. 2021

Sci Rep

Self Cite

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“…Many research innovations in breeding use machine learning, such as for maize [7] or corn [8], soybean [9], oil palm [10][11][12][13], wheat [14], yeast, and rice, [15] in order to understand the basic, most essential traits, and the flow of the study to fill the knowledge gap in the improvement of oil palm breeding studies. Therefore, this study aims to examine machine learning to analyze phenotypic data related to oil palm progenies data, which is likely to give different results from previous studies focused on genotypic data.…”

Section: Methodsmentioning

confidence: 99%

“…Chapman et al [10] predict the yield functions using machine learning (Bayesian Network Learning compared with Artificial Neural Network) to extract complex data. The research explains that machine learning algorithms have evolved in oil palm research, including the identification of recording yield errors [61], female classification inflorescence [11], oil palm plantations detection [13], fruit maturation [62], forecast fruit ripeness [63], and genome selection [48] in oil palm breeding programs.…”

Section: Rq3 How Can Machine Learning Benefit From the Phenotype Environment And Other Than Genetic Data?mentioning

confidence: 99%

Predicting Heritability of Oil Palm Breeding Using Phenotypic Traits and Machine Learning

et al. 2021

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Oil palm is one of the main crops grown to help achieve sustainability in Malaysia. The selection of the best breeds will produce quality crops and increase crop yields. This study aimed to examine machine learning (ML) in oil palm breeding (OPB) using factors other than genetic data. A new conceptual framework to adopt the ML in OPB will be presented at the end of this paper. At first, data types, phenotype traits, current ML models, and evaluation technique will be identified through a literature survey. This study found that the phenotype and genotype data are widely used in oil palm breeding programs. The average bunch weight, bunch number, and fresh fruit bunch are the most important characteristics that can influence the genetic improvement of progenies. Although machine learning approaches have been applied to increase the productivity of the crop, most studies focus on molecular markers or genotypes for plant breeding, rather than on phenotype. Theoretically, the use of phenotypic data related to offspring should predict high breeding values by using ML. Therefore, a new ML conceptual framework to study the phenotype and progeny data of oil palm breeds will be discussed in relation to achieving the Sustainable Development Goals (SDGs).

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“…Likewise, annual oil palm yield is predicted, and climate impacts are explored, in [66]. Finally, small-scale oil palm yield has been predicted with the help of historical yield data and multiple environmental factors [67]. The above-discussed literature is part of Table 5.…”

Section: Prediction/estimationmentioning

confidence: 99%

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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Using Bayesian networks to predict future yield functions with data from commercial oil palm plantations: A proof of concept analysis

Cited by 40 publications

References 24 publications

Crop response to El Niño-Southern Oscillation related weather variation to help farmers manage their crops

Crop response to El Niño-Southern Oscillation related weather variation to help farmers manage their crops

Predicting Heritability of Oil Palm Breeding Using Phenotypic Traits and Machine Learning

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

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