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Excessive irrigation can reduce cotton yield, but studies assessing the relative contribution of component physiological processes to yield loss are limited. The objective of the current experiment was to quantify irrigation‐induced yield loss attributable to Intercepted Photosynthetically Active Radiation (IPAR), Radiation Use Efficiency (RUE) and Harvest Index (HI). For three irrigation treatments (well‐watered, over‐irrigated, and dryland) during the 2018 and 2019 growing seasons, biweekly measurements of predawn leaf water potential (ΨPD) and light interception were taken along with measurements of biomass, lint yield, fibre quality and harvest index. Irrigation effects on yield were only observed during the 2019 season, and the results showed that ΨPD remained relatively high in both seasons and was rarely affected by irrigation treatment. A significant reduction in yield was observed for irrigated treatments, despite the dryland producing lower biomass. Any positive effects of IPAR and RUE on lint yield due to excess irrigation were offset by large declines in HI. We conclude that HI was the dominant contributor to yield loss due to excessive irrigation because reduced boll numbers and average boll mass were observed in plots with the greatest total above‐ground biomass.

Using UAV and multispectral images has contributed to identifying field variability and improving crop management through different data modeling methods. However, knowledge on application of these tools to manage peanut maturity variability is still lacking. Therefore, the objective of this study was to compare and validate linear and multiple linear regression with models using artificial neural networks (ANN) for estimating peanut maturity under irrigated and rainfed conditions. The models were trained (80% dataset) and tested (20% dataset) using results from the 2018 and 2019 growing seasons from irrigated and rainfed fields. In each field, plant reflectance was collected weekly from 90 days after planting using a UAV-mounted multispectral camera. Images were used to develop vegetation indices (VIs). Peanut pods were collected on the same dates as the UAV flights for maturity assessment using the peanut maturity index (PMI). The precision and accuracy of the linear models to estimate PMI using VIs were, in general, greater in irrigated fields with R2 > 0.40 than in rainfed areas, which had a maximum R2 value of 0.21. Multiple linear regressions combining adjusted growing degree days (aGDD) and VIs resulted in decreased RMSE for both irrigated and rainfed conditions and increased R2 in irrigated areas. However, these models did not perform successfully in the test process. On the other hand, ANN models that included VIs and aGDD showed accuracy of R2 = 0.91 in irrigated areas, regardless of using Multilayer Perceptron (MLP; RMSE = 0.062) or Radial Basis Function (RBF; RMSE = 0.065), as well as low tendency (1:1 line). These results indicated that, regardless of the ANN architecture used to predict complex and non-linear variables, peanut maturity can be estimated accurately through models with multiple inputs using VIs and aGDD. Although the accuracy of the MLP or RBF models for irrigated and rainfed areas separately was high, the overall ANN models using both irrigated and rainfed areas can be used to predict peanut maturity with the same precision.

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