Expert-Driven Spraying Phases and Deep Learning-Assisted Decision Support for Karshi/Qashqadaryo Irrigated Cotton Cultivation

Accurate spray timing is essential for reducing unnecessary pesticide use in irrigated cotton production. This study developed and evaluated a locally calibrated six-stage Spraying Phase (SP) scale for the Karshi/Qashqadaryo production context. The scale was established through a two-round moderated consensus process involving 16 expert panelists representing this production context. A screened dataset of 14,400 non-standardized smartphone images was used to train and evaluate a ResNet-50 convolutional neural network (CNN) for SP-stage classification. Field validation was conducted at the Karshi Engineering and Economics Institute during the 2023 and 2024 seasons using an internally controlled randomized complete block design (RCBD)-style paired comparison of SP-based and BBCH-based spray timing. The CNN achieved 93.0% test accuracy. The mean number of pesticide applications was descriptively lower under SP-guided scheduling than under BBCH-based scheduling (3.75 versus 4.88 applications per season; −23.1%). For the inferentially evaluated outcomes, crop-protection cost decreased by 21.2%, the Environmental Risk Index decreased by 21.6%, and plot-level lint-equivalent yield increased by 4.5%. These findings support SP-guided timing as a promising locally calibrated decision-support approach under the tested Karshi/Qashqadaryo conditions; broader use requires multi-site, multi-cultivar, multi-season, device-stratified, and BBCH-level validation, together with technical deployment testing and implementation-cost assessment.