Classification and Identification of Pinecone Mulching in Blueberry Cultivation Based on Crop Leaf Characteristics and Hyperspectral Data

Abstract

While crushed pinecone mulch holds promise as a beneficial material for blueberry cultivation, research on its effectiveness remains limited. Crop leaf characteristics can serve as parameters for assessing mulching effects, although there are several limitations, including the need to analyze various distinct characteristics separately. The combination of hyperspectral data and machine learning techniques is expected to enable the selection of only the most important features among these characteristics. In this study, we investigated the impact of various mulching treatments utilizing pine tree byproducts, including crushed pinecones. Mulching variations included non-mulching (NM), crushed pinecones (PCs), a mixture of crushed pinecones and sulfur (PCS), pine needles (PNs), and sulfur treatment (S). Conventional methods were employed to measure leaf growth (length and width) and physiological characteristics (chlorophyll content, chlorophyll fluorescence, and stomatal conductance). Hyperspectral reflectance was also measured, and classification models using Partial Least Squares Discriminant Analysis (PLS-DA) and eXtreme Gradient Boosting (XGBoost) were developed for crop characteristics, vegetation indices (VIs), visible and near-infrared (VNIR), and short-wave infrared (SWIR). The results showed that using crushed pinecones as the sole mulching material for blueberries, without sulfur treatment, had a positive impact on blueberry growth. The PC treatment exhibited a dual effect on plant growth by lowering the soil pH to 5.89 and maintaining soil moisture within the range of 26.33–35.20%. We observed distinct differences in soil inorganic nutrient content, with higher concentrations of organic matter, total nitrogen, and available P2O5 and K+, which positively influenced blueberry growth. Mulching treatments demonstrated superior physiological characteristics, with two classification models identifying stomatal conductance (gs) as a key parameter influencing treatment classification (VIP scores > 1 rank: 3, variable score rank: 1). The photochemical reflectance index (PRI) emerged as a major parameter among VIs, showing potential for measuring water stress (VIP scores > 1 rank: 2, variable score rank: 1). In the SWIR PLS-DA model, wavelength peaks were mainly observed in the O-H overtone (1410 nm, 1450 nm, 1930 nm, 1940 nm, and 2100 nm). Overall, crushed pinecones were found to positively impact the initial growth of blueberries by enhancing water status (plant respiration).

Keywords

• highbush blueberry
• pinecone mulching
• sulfur treatment
• hyperspectral reflectance
• classification model