## 3 Palm Oil Background
### Factors Affecting Palm Oil Growth and Quality
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Components** & **Features Data** & **Factor Category** \\ \hline Soil and Fertilisation & Levels of potassium (K), nitrogen (N), phosphorus (P), magnesium (Mg), calcium (Ca), nutrient supplements of groundwater, topography and slope, consumption of chemical fertilisers, consumption of agricultural pesticide, type and texture of the soil, and planting density. & Environment (E) \\ \hline Climatic & Precipitation, humidity, maximum temperature, minimum temperature, mean temperature, rainfall, irrigation carbon dioxide concentration, solar radiation, water measurements, and waterlogging. & Environment (E) \\ \hline UAV for Canopy Surveillance & Tree crown, stems length, tree colours, crown size, bare land, and water. & \\ \hline Yield & Low lipase, high stearic and high oleic, low free fatty acids content and high iodine value, oil quality, quality, yield, oil to-dry mesocarp (O/DM), total oil yield per palm (O/P), oil content in mesocarp, oil-to-wet mesocarp (O/WM), palm oil yield (PO), FFB, yield, height, and high caroten. & \\ \hline Fruit & Mesocarp-to-Fruit (M/F), Shell-to-Fruit (S/F), Kernel-to-Fruit (K/F), oil-bearing mesocarp, shell thickness, reasonable stalk length, good fruit set, and low parthenocary, fruit form, fruit quality, fruit colour virescent (VIR), and fruit ripeness. & Phenotype (P) \\ \hline Bunch & Annual cumulative bunch number (BN), annual average bunch weight (ABW), annual cumulative bunch production (FFB), bunch production, bunch index, pulp-to-fruit ratio (FF), fruit-to-bunch (F/B), oil to bunch (O/B) oil extraction rate (OER), and oil-to-pulp ratio (OP), average bunch number per hectare (BUNCH_HA), and bunch weight (BW). & Phenotype (P) \\ \hline Crop Samples & Flavonoid, anthocyanin content, fruit colour, fruit size, hue, saturation, intensity, contour lines, and blue-to-red fluorescence ratio (BRR_FRF). & Phenotype (P) \\ \hline Tree Detection & Positive/negative histogram of oriented gradients (HOG), crown size, images of palm oil, built-up, bare land, water, and forest. & \\ \hline Vegetative & Total dry matter, plant architecture, leaf length, number of leaves per plant, plant height, height increment (HT), and frond length (FL). & \\ \hline Estimation & Nutrient content and plant height, shell thickness, mantled somaclonal variation together with gene networks for oil biosynthesis, drought and cold tolerance, in vitro regeneration potential, lipase activity, caroten and vitamin E contents, FA composition and iodine value, fruit shell thickness, fatty acid (FA) profile, fruit age, plant life, age factor, average value, daily CPO prices, monthly closing prices of oils, benefits, plant scale index, sex determination, infloresence abortion, foliar nutrient composition, FFB yield, growth, and respiration. & \\ \hline Disease detection & Bud and spear rot, sudden wilt, red ring disease and basal stem rot disease spectral reflectance, leaves and stem colours, freckle (_Cerospora ealeidis_), blast (_Pythium splendens_ and _Rhizcedonia lamellifera_), vascular wilt (_Fusarium oxysporum f_. sp. _elezidis_), and ganoderma trunk rot (_Ganoderma_ spp.). & \\ \hline Others & Tree crowns and categorical features, thermal images, quantitative features, oil per palm, chlorophyll-sensitive wavelengths, and electrical properties of leaves. & \\ \hline Breeding & DNA and tissues of the dura, pisifera, tenera; breeding-Deli, AVROS, Yangambi, Calabar, Ekona, Binga, and La Me. & Genotype \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Categorical variables. The review results based on this review will be highly significant given that essential factors are incorporated into the prediction process via big data. However, more research is required to determine if big data can improve the prediction of palm oil performance.