Garlic (Allium sativum) feature-specific nutrient dosage based on using machine learning models

Abstract

Brazil presents large yield gaps in garlic crops partly due to nutrient mismanagement at local scale. Machine learning (ML) provides powerful tools to handle numerous combinations of yield-impacting factors that help reducing the number of assumptions about nutrient management. The aim of the current study is to customize fertilizer recommendations to reach high garlic marketable yield at local scale in a pilot study. Thus, collected 15 nitrogen (N), 24 phosphorus (P), and 27 potassium (K) field experiments conducted during the 2015 to 2017 period in Santa Catarina state, Brazil. In addition, 61 growers’ observational data were collected in the same region in 2018 and 2019. The data set was split into 979 experimental and observational data for model calibration and into 45 experimental data (2016) to test ML models and compare the results to state recommendations. Random Forest (RF) was the most accurate ML to predict marketable yield after cropping system (cultivar, preceding crops), climatic indices, soil test and fertilization were included features as predictor (R2 = 0.886). Random Forest remained the most accurate ML model (R2 = 0.882) after excluding cultivar and climatic features from the prediction-making process. The model suggested the application of 200 kg N ha-1 to reach maximum marketable yield in a test site in comparison to the 300 kg N ha-1 set as state recommendation. P and K fertilization also seemed to be excessive, and it highlights the great potential to reduce production costs and environmental footprint without agronomic loss. Garlic root colonization by arbuscular mycorrhizal fungi likely contributed to P and K uptake. Well-documented data sets and machine learning models could support technology transfer, reduce costs with fertilizers and yield gaps, and sustain the Brazilian garlic production.

A comprehensive review on genomic resources in medicinally and industrially important major spices for future breeding programs: Status, utility and challenges

“…Being an expensive spices, saffron adulteration is one of the major issues rectified by visual computing and machine learning algorithms ( Husaini et al, 2022 ). Machine learning has been widely used for feature-specific nutritional dosage detection and field classification detection for garlic ( Hahn et al, 2022 ), grading in Cardamom ( Jose and Krishnan, 2015 ), pest and disease management in chili ( Ahmad Loti et al, 2021 ), simulation of isoquercitrin in Coriander ( Usman et al, 2021 ), Mono-disperse carbon quantum dot characterization in fennel ( Dager et al, 2019 ), detection of Fusarium in black pepper ( Karadag et al, 2020 ), effects of peptide-protein interaction as drug targets in Cinnamon ( Wang et al, 2021 ), and clove bud origin ( Gunawan and Kresnowati, 2020 ). Furthermore, adulteration is a common practice in the spice market, and commercialization is largely managed by machine learning for turmeric and ginger powder ( Jahanbakhshi et al, 2021 ).…”

Customized nutrient management of onion (Alium cepa) agroecosystems

“…Benchmark blobs were also called 'Enchanting Islands' 55 , 'Humboldtian loci' 14 , and 'Ilhas Encantadas' in Portuguese 15 . This emphasizes the need to compare tissue nutrient compositions of diagnosed specimens to those of true negative specimens growing under similar local conditions to account for genetics×environment interactions [56][57][58] .…”

Rapid discrimination of geographical origin of garlic (Allium sativum L.): A metabolomic approach applied to paper spray mass spectrometry data

“…They exhibit slight flavor and appearance differences suitable for distinct culinary applications. 3,4 Chinese garlic has a white, papery skin, a strong, pungent flavor, and minor bulbs. On the other hand, Brazilian garlic is characterized by its purplish skin and is more savory, with more robust acid notes and thicker bulbs.…”