Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

Abstract

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

Wheat physiology predictor: predicting physiological traits in wheat from hyperspectral reflectance measurements using deep learning

“…Most prior work [6,8,10,11,[13][14][15][16][17][18][19][20][21] on mapping hyperspectral reflectance based measurements of various plants to physiological traits and leaf biochemistry uses PLSR to develop predictive algorithms. PLSR has been used for studying diverse traits such as sucrose, reducing sugar and total sugar dynamics [18], leaf water status [17,19], salinity stress [20] and leaf nutrient contents [21]. Diverse species studied include tobacco [6,8], tree species [10,13], soybean [11], maize [14], wheat [15], rice [20], okra [16] and mango [21].…”

“…Most prior work [ 6 , 8 , 10 , 11 , 13 – 21 ] on mapping hyperspectral reflectance based measurements of various plants to physiological traits and leaf biochemistry uses PLSR to develop predictive algorithms. PLSR has been used for studying diverse traits such as sucrose, reducing sugar and total sugar dynamics [ 18 ], leaf water status [ 17 , 19 ], salinity stress [ 20 ] and leaf nutrient contents [ 21 ]. Diverse species studied include tobacco [ 6 , 8 ], tree species [ 10 , 13 ], soybean [ 11 ], maize [ 14 ], wheat [ 15 ], rice [ 20 ], okra [ 16 ] and mango [ 21 ].…”

“…PLSR has been used for studying diverse traits such as sucrose, reducing sugar and total sugar dynamics [ 18 ], leaf water status [ 17 , 19 ], salinity stress [ 20 ] and leaf nutrient contents [ 21 ]. Diverse species studied include tobacco [ 6 , 8 ], tree species [ 10 , 13 ], soybean [ 11 ], maize [ 14 ], wheat [ 15 ], rice [ 20 ], okra [ 16 ] and mango [ 21 ]. Data sets used are usually small (i.e., just a few hundred samples) in most studies, hence resulting in overfitting to the training data in the model, which results in prediction within the training data but not in unseen samples, is a major problem that needs to be avoided.…”

Characterizing Leaf Nutrients of Wetland Plants and Agricultural Crops with Nonparametric Approach Using Sentinel-2 Imagery Data

“…Although a large and growing body of the literature has illustrated that vegetation indices outperform general wavebands in estimating vegetation attributes [80][81][82], in this study we combined the vegetation indices with spectral bands considering that very few of the aforementioned studies were conducted in wetlands. Vegetation indices were used in this study because of their robustness as illustrated in the literature [50,56,57,80,[83][84][85]. They derive their robustness from two or more wavebands.…”

Using Unmanned Aerial Vehicle and Ground-Based RGB Indices to Assess Agronomic Performance of Wheat Landraces and Cultivars in a Mediterranean-Type Environment

“…Some of them have shown a root mean square error (RMSE) ranging from 0.57 to 0.97 t/ha for predicting yield in wheat [18,19]. Other methodologies also use machine-learning regressions, chemometrics, radiative transfer models, photogrammetry, or hybrid approaches to estimate vegetation traits [20][21][22]. On the other hand, far-infrared (thermal) radiation and LIDAR sensors have been respectively used to estimate plant water status [23] and to characterize the architectural features [24].…”