Abstract
Simultaneous and timely assessment of growth and water status-related plant traits is critical for precision irrigation management in arid regions. Here, we used proximal hyperspectral sensing tools to estimate biomass fresh weight (BFW), biomass dry weight (BDW), canopy water content (CWC), and total tuber yield (TTY) of two potato varieties irrigated with 100%, 75%, and 50% of the estimated crop evapotranspiration (ETc). Plant traits were assessed remotely using published and newly constructed vegetation and water spectral reflectance indices (SRIs). We integrated genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS) models to predict the measured traits based on all SRIs. The different plant traits and SRIs varied significantly (p < 0.05) between the three irrigation regimes for the two varieties. The values of plant traits and majority SRIs showed a continuous decrease from the 100% ETc to the 50% ETc. Water-SRIs performed better than vegetation-SRIs for estimating the four plant traits. Almost all indices of the two SRI types had a weak relationship with the four plant traits (R2 = 0.00–0.37) under each irrigation regime. However, the majority of vegetation-SRIs and all water-SRIs showed strong relationships with BFW, CWC, and TTY (R2 ≥ 0.65) and moderate relationships with BDW (R2 ≥ 0.40) when the data of all irrigation regimes and varieties were analyzed together for each growing season or the data of all irrigation regimes, varieties, and seasons were combined together. The ANFIS-GA model predicted plant traits with satisfactory accuracy in both calibration (R2 = 1.0) and testing (R2 = 0.72–0.97) modes. The results indicate that SRI-based ANFIS models can improve plant trait estimation. This analysis also confirmed the benefits of applying GA to ANFIS to estimate plant responses to different growth conditions.
Highlights
The primary objectives of this study were (i) to quantify the effects of different irrigation regimes on different plant traits related to growth, production, and water states of two potato varieties, (ii) to remotely assess the different plant traits of each potato crop variety across all irrigation regimes, for each irrigation regime across two varieties, and across all conditions using different spectral reflectance indices (SRIs); (iii) to evaluate the performance of adaptive neuro-fuzzy inference system (ANFIS)-genetic algorithm (GA) models based on all SRIs to predict the different plant traits across all conditions
The four plant traits (BFW, biomass dry weight (BDW), canopy water content (CWC), and total tuber yield (TTY)), showed significant differences (p < 0.05) between three irrigation regimes and this was true for both potato varieties
The results showed that the majority of indices of the two types of SRIs were more effective for estimating biomass fresh weight (BFW), CWC, and TTY than BDW; this was true for both varieties (Table 4)
Summary
The potato (Solanum tuberosum L.) is one of the world’s most economically relevant plant species It is the fourth most important food crop globally behind rice, wheat, and maize [6]. In developing countries, it is an affordable and rich source of starch that can be cultivated under a wide range of climatic conditions. Several studies have reported that exposure of potato plants to deficit water stress especially during tuber bulking and ripening growth stages can lead to over 50% reduction in tuber yield [8,9,10,11,12,13]
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