Hyperspectral remote sensing is considered to be an effective tool in crop monitoring and estimation of biomass. Many of the previous approaches are from single year or single date measurements, even though the complete crop growth with multiple years would be required for an appropriate estimation of biomass. The aim of this study was to estimate the fresh matter biomass (FMB) by terrestrial hyperspectral imaging of the three crops (lablab, maize and finger millet) under different levels of nitrogen fertiliser and water supply. Further, the importance of the different spectral regions for the estimation of FMB was assessed. The study was conducted in two experimental layouts (rainfed (R) and irrigated (I)) at the University of Agricultural Sciences, Bengaluru, India. Spectral images and the FMB were collected over three years (2016)(2017)(2018) during the growing season of the crops. Random forest regression method was applied to build FMB models. R 2 validation (R 2 val ) and relative root mean square error prediction (rRMSEP) was used to evaluate the FMB models. The Generalised model (combination of R and I data) performed better for lablab (R 2 val = 0.53, rRMSEP = 13.9%), maize (R 2 val = 0.53, rRMSEP = 18.7%) and finger millet (R 2 val = 0.46, rRMSEP = 18%) than the separate FMB models for R and I. In the best derived model, the most important variables contributing to the estimation of biomass were in the wavelength ranges of 546-910 nm (lablab), 750-794 nm (maize) and 686-814 nm (finger millet). The deviation of predicted and measured FMB did not differ much among the different levels of N and water supply. However, there was a trend of overestimation at the initial stage and underestimation at the later stages of crop growth.to local water availability [2]. Irrigated crop production is a major contributor to the green revolution, which has enabled the country to be self-sufficient [3], accompanied by fertiliser application and other inputs in semi-arid parts of India. Timely fertiliser application with water supply is essential for a successful crop. Spectral data from Remote Sensing (RS) have been studied for many years for an adequate assessment of nutrient and water variability for yield optimisation [4][5][6] RS can be an effective tool in monitoring crop production [7-9] and estimating yield [10,11]. Early estimation of yield may allow better planning and forecasting the market prices and support food security based on the regional, national and global demand and supply. RS allows collecting information about crop production using non-destructive methods [12] on a large scale for many fields at the same time. Hyperspectral (HS) RS provides continuous narrow spectral data from 400 to 2500 nm and have been proved to capture the variations in spectral response of the crop for the detection of nitrogen (N) content [13,14], biomass [15] and water stress [6,16]. Development of HS sensors and their application in estimating crop biomass from multi-year data [17] has gained increasing attention in the recen...

A field experiment was conducted during rabi, 2012-13 and 2013-14 on red sandy clay loam soil to study the effect of foliar application of water soluble fertilizers on chickpea. The experiment consisted of fifteen treatments which were replicated thrice in RCB Design. Results indicated that spray of water soluble fertilizer (19:19:19) at 1.5 % concentration at flowering and pod development stage along with basal application of fertilizers (13.5: 25: 25 kg N, P 2 O 5 , K 2 O) significantly increased growth attributes, yield and benefit cost ratio in chickpea cultivation. Further increase in spray concentration up to 2.0 % did not significantly improve the performance of chickpea.

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