In modern agriculture, remote sensing offers efficient tool for enhancing crop production while satisfying sustainability requirements by diagnosing plant nutritional status. Indeed, remote sensing is widely used for early information management about crops fertilization needs, yield forecasting, diseases identification and decision support system from plant leaf to plant canopy and farmland to landscape scale. It is therefore quite rightly that methods derived from remote sensing are highly regarded as key components of Fertilizers Best Management Practices (FBMPs) [1] . According to statistical prevision, world population will grow over 9 billion by 2050 [2] [3] . This situation may increase pressure on world agriculture by the use of greater amount of chemical fertilizer. To overcome future challenges in food supply for this expected world population, locks and limits in the applications of field remote sensing technology have to be solved.

The availability of nutrients represents a limiting parameter of great importance in the evaluation of crop yields [1] . Macronutrients are plant nutrients required in important amounts and are constituted by nitrogen (N), potassium (K), calcium (Ca), magnesium (Mg), phosphorous (P), and sulfur (S) [4] . N, P, K, and Mg are used in photosynthesis and respiration, whereas Ca serves to cell division and to cell walls construction [5] . As yet, dynamics of progress on field remote sensing methods currently makes proximal portable or mounted devices directly used in the field to measure N content with acquisition of fluorescence and reflectance data [6] [7] [8] . Nevertheless, in-field determinations of other mineral nutrients are not as effective as in the case of nitrogen. Nutrients such as P, K and S are particularly concerned [3] . About developments in fast spectroscopy for plant mineral analysis, it appears that the use of visible data, combined with the lowest part of near infra-red range had resulted in good calibration models especially in K [9] as well as in Ca and Mg cases [10] . P prediction was poor whereas authors in these studies have employed full spectral range (400 - 1000 nm). Using Vis-NIR hyperspectral imaging, Zhang et al. [11] gave acceptable prediction performance of leaf P macronutrient content in region of interest. Since correlations between polyphenol and mineral content in olive leaves were established in Cetinkaya et al. [12] study, we might expect better prediction performance of leaf macronutrient content by using reflectance additionally with fluorescence data.

Okra (Abelmoschus esculentus (L.) Moench. syn. Hibiscus esculentus L.) is a rich source of protein, vitamin, magnesium, potassium, manganese, sodium, calcium, iron, copper and zinc [13] [14] [15] . Apart from its rich nutritional aspects, it has been reported therapeutic benefits (antidiabetic, antipyretic, diuretic, antispasmodic, etc.) [16] . This vegetable is widely cultivated within tropical and subtropical regions where nutrients requirements for N, P₂O₅ and K₂O are reported to be 79, 32 and 89 kg∙ha⁻¹ respectively for a yield of 20 t∙ha⁻¹ [17] . Although these amounts vary with factors such as: cultivar, plant density, soil type, whether the crop is irrigated or not, the climate and other environmental conditions [18] , higher productivity of Okra has been obtained with manure fertilization [19] [20] [21] . In West Africa, the gumbos occupy the second place of vegetable production after tomatoes [22] . Two okra species are cultivated in Ivory Coast: Abelmoschus esculentus and Abelmoschus caillei whose average yield is up to 6 t∙ha⁻¹ and potential yields vary between 11 and 13 t∙ha⁻¹ [23] .

Chemistry or biochemistry data retrieval in spectroscopy measurement is accurately performed in multivariate calibration methods. These procedures are largely known as chemometrics in which efforts are made for developing mathematical and statistical methods to extract relevant, useful and efficient information from raw spectral data. In the optimization of chemometric process, steps such as spectral pretreatment, variable selection and latent factors are highly cases sensitive [24] . Especially in quantitative spectroscopy, single wavelengths selection has been shown to improve precision and accuracy in the calibration process [25] . Moreover, identification of effective wavelengths could offer possibilities to develop non-destructive macronutrient diagnosis devices for crops monitoring. Recently, new trends in chemometrics have highlighted IRIV-PLS methods which demonstrated superior performances in front of other successful variable selection algorithm particularly GA-PLS, MC-UVE-PLS and CARS [26] . IRIV method is part of MPA-based method. This algorithm consists in generating sub-datasets by using Binary Matrix Sampling (BMS) and to find out strongly informative, weakly informative, uninformative and interfering variables, from statistical analysis in the variable space. The most interesting variables retained after backward elimination procedure are those which are strong and weak.

In the present study, we demonstrate potential of multi-excitation fluorescence combined with active reflectance for sensing yield and leaves macronutrients contents. This main objective is twofold: 1) to identify the best phenologic stage for pods yield prediction; and 2) to develop predictive models of N, P, K, and Ca concentration in okra leaves.

The seeds of gumbo from the variety GB1230 provided by the Centre National de Recherche Agronomique (CNRA) were used in this study. The fertilizer experimentation was conducted at National Polytechnic Institute (INPHB) experimental farm on a manually cleared area of dimensions 39 m × 16 m located at altitude 229 m, latitude 06˚53'17.5''N and longitude 05˚13'19.6''W. The climate during the experimental period (mid-August to mid-November 2014) on site registered mean values of 27˚C temperature, 78% humidity and 1008.5 hPa pressure. Three composite surface soil samples (0 - 15 cm) were collected in three main parts of field before sowing. The samples were dried in open air laboratory for 15 days and clods were crushed by hand and gravels removed. Subsequently, the soil was slightly crushed and passed through a 2 mm calibrated sieve. Analyses were performed on soil fractions smaller than 2 mm. pH was measured in a water-soil (ratio 2/5) solution with a pH-meter. The methods of Walkley and Black [27] , Kjeldahl [28] and Thomas [29] were respectively used for the determination of organic carbon, total nitrogen and organic ammonium. Cation exchange capacity (CEC) and exchangeable cations (Ca²⁺, Mg²⁺, Na⁺, K⁺) were extracted by the ammonium acetate method [29] . Total phosphorus is extracted by perchloric acid while assimilable phosphorus was estimated with Olsen method modified Dabin. Chemical properties of samples analysed indicate a very low fertile soil with fairly rich organic matter according to the critical classes of chemical elements [30] . The mean soil pH was 6.2 and some of the other mean values obtained were as follows: total nitrogen, 0.06%; organic carbon, 0.64%; NH 4 + , 0.25% ; assimilable and available phosphorus, 25 ppm and 294 ppm. CEC mean value is 3.31 cmol∙kg⁻¹ and the values obtained for Ca²⁺, Mg²⁺, K⁺ and Na⁺ were 0.829, 0.330, 0.078 and 0.083 ppm, respectively.

The experiment consisted of three 14 m × 11 m blocks; each block was divided into twelve plots of 5 m × 1.5 m including 15 plants, with an alley of 2 m between the blocks and 1 m within the plots. The experiment was laid out in a randomised complete block design (RCBD), with twelve treatments, and each treatment was replicated three times. Treatments consisted of a combination of N, P and K, each one at three levels supplemented with three reference treatments at the same level for all factors as shown in Table 1.

Fertilizers were prepared using urea (45% N), potassium chloride (60% K₂O) and tricalcic phosphate (27% P₂O₅). They were applied once and locally for each plant of a plot at twenty days after sowing corresponding to 2 - 3 leaves stage. Each fertilizer proportion was calculated on the basis of the optimal requirement per plant knowing the elementary surface of application according the formula [31] :

q = x × S C × 100 (1)

With x: fertilizer proportion per hectare; S: elementary surface (m²); C: content of fertilizing unit (%).

The experimental setup was detailed in a previous study [32] and was adaptation from Brydegaard et al. [33] one. It used an USB4000 spectrometer Ocean Optics,