Impact of AI based Irrigation Scheduling Approaches and Drip Irrigation Methods on Yield of Chilli (Capsicum annum L.) and Chemical Properties of Soil

K. Bhavitha, M. L. Pasha, V. Ramulu, T. R. Prakash, P. Rajaiah, P. Revathi
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Abstract

Aim: To assess the effect of AI based irrigation scheduling approaches and drip irrigation methods on soil chemical properties and yield  in chilli. Study Design: The study employs drip irrigation methods as the main plots and irrigation scheduling approaches as the subplots. A split plot design was chosen as suitable design because the main plots (drip irrigation methods) need a bigger plot sizes and subplots (irrigation scheduling approaches) requires more precise results with smaller plot sizes. Place and Duration of Study: Water Technology Centre field, College Farm, College of Agriculture, Rajendranagar, Hyderabad during rabi 2022-23 (first year) and 2023-24 (second year). Methodology: The investigation consisted of two drip irrigation methods as main plots and four irrigation scheduling approaches as subplots with total of 8 treatment combinations replicated thrice. Data recorded on various parameters was subjected to scrutiny by ANOVA technique for split plot design concept. Results: Green (fresh) fruit and stalk yield was found to be significantly higher under subsurface drip (41859 and 5037 kg ha-1) among drip irrigation methods; whereas, among irrigation scheduling approaches, ET sensor based irrigation triggering resulted in significantly higher green (fresh) fruit and stalk yield (43139 and 5196 kg ha-1) followed by irrigation scheduling at 1.0 Epan by manual (control) (42235 and 5065 kg ha-1). The post-harvest soil chemical properties were found to be non-significantly influenced by drip irrigation methods and irrigation scheduling approaches. Conclusions: Subsurface drip and ET sensor based irrigation triggering resulted in higher fruit and stalk yield which might be recommended for conserving irrigation water and reducing labour use. Whereas, the drip irrigation methods and irrigation scheduling approaches did not exert any significant influence on chemical properties of post-harvest soil.
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基于人工智能的灌溉调度方法和滴灌方法对辣椒(Capsicum annum L.)产量和土壤化学性质的影响
目的:评估基于人工智能的灌溉调度方法和滴灌方法对辣椒土壤化学性质和产量的影响。研究设计:本研究采用滴灌方法为主小区,灌溉调度方法为副小区。由于主小区(滴灌方法)需要较大的小区面积,而子小区(灌溉调度方法)需要较小的小区面积来获得更精确的结果,因此选择了分小区设计作为合适的设计。研究地点和时间:2022-23 年(第一年)和 2023-24 年(第二年),海得拉巴 Rajendranagar 农业学院学院农场水技术中心田间。调查方法:调查以两种滴灌方法为主地块,四种灌溉调度方法为副地块,共有 8 种处理组合,重复三次。采用方差分析技术对分小区设计概念下记录的各种参数数据进行了仔细分析。结果在滴灌方法中,地表下滴灌的绿色(新鲜)果实和果柄产量(41859 和 5037 千克/公顷-1)明显更高;而在灌溉调度方法中,基于蒸散发传感器的灌溉触发导致绿色(新鲜)果实和果柄产量(43139 和 5196 千克/公顷-1)明显更高,其次是人工(对照)1.0 Epan 的灌溉调度(42235 和 5065 千克/公顷-1)。滴灌方法和灌溉调度方法对采后土壤化学性质的影响不显著。结论地表下滴灌和基于蒸散发传感器的灌溉触发可提高果实和茎秆产量,可推荐用于节约灌溉用水和减少劳动力使用。而滴灌方法和灌溉调度方法对采后土壤的化学特性没有显著影响。
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