Dynamic diagnosis and reliability assessment of potassium nutrition in potatoes by fusing multispectral information and texture information

Abstract

Potassium is a key element for potato growth and development, playing an important role in enhancing stress resistance, improving tuber quality, and ensuring stable high yields. Therefore, precise and dynamic management of potassium is of vital importance. This study conducted a two-year field experiment on potatoes, setting three irrigation levels (W1: 100 % ETC, W2: 80 % ETC, W3: 60 % ETC) and five potassium fertilizer gradients (K0: 0 kg·ha⁻¹, K1: 100 kg·ha⁻¹, K2: 200 kg·ha⁻¹, K3: 300 kg·ha⁻¹, K4: 400 kg·ha⁻¹). The results showed that the W1K3 treatment achieved the highest potato yield of 59,482.16 kg·ha⁻¹, while excessive potassium application (K4) led to a yield reduction. Subsequently, a critical potassium concentration dilution curve was constructed, and the Measured Potassium Nutrition Index (KNIM) and Theoretical Potassium Nutrition Index (KNIT) were calculated. Under the K3 gradient, the KNI values across all irrigation levels were close to 1, corresponding to the maximum potato yield, with a strong correlation observed between KNIM and KNIT (r > 0.87). Concurrently, multispectral data were collected to construct vegetation indices, extract image texture features, and generate texture indices through random combination of these features. The results indicated that most vegetation indices and some texture features were significantly correlated with KNI (P < 0.05), while all constructed texture indices exhibited extremely significant correlations with KNI (P < 0.01). Different variable combinations were used as input variables to develop KNI prediction models based on machine learning. The optimal performance was achieved by the model integrating spectral information and texture indices with the Random Forest (RF) algorithm, which yielded validation set results for KNIM and KNIT as follows: determination coefficients (R²) of 0.824 and 0.795, root mean square errors (RMSE) of 0.061 and 0.064, and mean relative errors (MRE) of 4.817 % and 5.787 %, respectively. As a theoretically calculated index, the correlation between predicted KNIT values and relative yield further confirmed the validity of KNIT, while the generated potassium nutrition status maps intuitively and clearly demonstrated the feasibility and accuracy of KNIT. This study successfully realized non-destructive and real-time prediction of KNIT through machine learning models, providing a quantitative basis for accurately analyzing the dynamic potassium demand of plants. It holds significant practical implications for promoting the green and sustainable development of the potato industry.

Plant phenotyping relevance

ジャガイモのカリウム栄養状態を対象に、マルチスペクトル情報・画像テクスチャ・機械学習を統合してKNIを非破壊かつリアルタイムに推定し、検証しているため、表現型取得・推定手法が中心である。

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