Assessment of smart irrigation controllers under subsurface and drip-irrigation systems for tomato yield in arid regions

Abstract

Here, two types of smart irrigation controllers intended to reduce irrigation water are investigated under Saudi Arabia’s present water crisis scenario. These controllers are specially made for scheduling irrigation and management of landscaping. Consequently, the aim of this study is to adapt the efficient automated controllers to tomato crops, and for extension to other similar agricultural crops. The controllers are based on evapotranspiration and have been shown to be promising tools for scheduling irrigation and quantifying the water required by plants to achieve water savings. In particular, the study aims to evaluate the effectiveness of these technologies (SmartLine SL 1600and Hunter Pro-C) in terms of the amount of irrigation applied and compare them with conventional irrigation scheduling methods. The smart irrigation systems were implemented and tested under drip irrigation and subsurface irrigation for tomato (cv. Nema) in an arid region. The results revealed significant differences between the three irrigation-scheduling methods in both the amount of applied water and yield. For example, each 1 mm water depth applied to the tomato crop via subsurface (or drip) irrigation by SmartLine, Hunter Pro-C, and the control system yielded 129.70 kg (70.33 kg), 161.50 kg (93.47 kg), and 109.78 kg (108.32 kg), respectively. Generally, the data analysis indicates that the Hunter Pro-C system saves water and produces a higher yield with the greatest irrigation water-use efficiency (IWUE) of the irrigation scheduling methods considered. Moreover, the results indicate that the subsurface irrigation system produced a higher yield and IWUE than the drip system.

Efficient data-centric pest images identification method based on Mahalanobis entropy for intelligent agriculture

“…8 Although the previous data evaluation focused on the quality of imaging and subjective perception, 9 the current data evaluation focuses more on the amount of information in the image. These techniques are also gradually used in smart agriculture, including the classification of plant species, 10 identification of crop diseases and pests, 11 collaborative irrigation 12 with multiple agents, 13 etc. Abdullah 14 proposed an intelligent agricultural system with the help of which users can analyze the environment and intervene.…”