Abstract
Advances in proximal hyperspectral sensing tools, chemometric techniques, and data-driven modeling have enhanced precision irrigation management by facilitating the monitoring of several plant traits. This study investigated the performance of remote sensing indices derived from thermal and red-green-blue (RGB) images combined with stepwise multiple linear regression (SMLR) and an integrated adaptive neuro-fuzzy inference system with a genetic algorithm (ANFIS-GA) for monitoring the biomass fresh weight (BFW), biomass dry weight (BDW), biomass water content (BWC), and total tuber yield (TTY) of two potato varieties under 100%, 75%, and 50% of the estimated crop evapotranspiration (ETc). Results showed that the plant traits and indices varied significantly between the three irrigation regimes. Furthermore, all of the indices exhibited strong relationships with BFW, CWC, and TTY (R2 = 0.80–0.92) and moderate to weak relationships with BDW (R2 = 0.25–0.65) when considered for each variety across the irrigation regimes, for each season across the varieties and irrigation regimes, and across all data combined, but none of the indices successfully assessed any of the plant traits when considered for each irrigation regime across the two varieties. The SMLR and ANFIS-GA models gave the best predictions for the four plant traits in the calibration and testing stages, with the exception of the SMLR testing model for BDW. Thus, the use of thermal and RGB imaging indices with ANFIS-GA models could be a practical tool for managing the growth and production of potato crops under deficit irrigation regimes.
Highlights
Water shortage is one of the key challenges for sustainable agriculture in arid and semiarid countries, under abrupt climate change, as the agricultural sector is highly vulnerable to continuously changing climatic patterns [1]
The results showed that normalized relative canopy temperature (NRCT) in combination with COM, NRCT together with the R, G, and B band percentages, NRCT alone, and Vegetative index (VEG) alone explained most of the variation in biomass fresh weight (BFW) (89%), biomass water content (BWC) (92%), total tuber yield (TTY) (84%), and biomass dry weight (BDW) (47%), respectively (Table 3)
Thermal and RGB imaging indices were combined with multivariate and data-driven modeling in this study in an attempt to non-destructively estimate the growth, water status, and yield of potato crops exposed to different drip irrigation regimes
Summary
Water shortage is one of the key challenges for sustainable agriculture in arid and semiarid countries, under abrupt climate change, as the agricultural sector is highly vulnerable to continuously changing climatic patterns [1]. Dramatic climatic changes in the future are expected to increase water scarcity by approximately 20% globally [2]. 2021, 13, 1679 countries rely entirely on irrigation and consume approximately 75% of the renewable freshwater resources in these countries. Most importantly, these irrigated lands play an important direct role in global food security, contributing approximately 40% of the total food produced worldwide despite covering only 20% of the total cultivated land as well as it contribute as much as 80%, 70%, 50%, and 50% of food production in Pakistan, China, India, and Indonesia, respectively [3]. Limited irrigation water supplies for this sector will pose a very serious threat to future global food security. It is necessary to develop water-saving strategies that maximize crop production per unit of irrigation water supply rather than per unit of area [4,5]
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