The use of new technologies to meet the demands of the agricultural market is increasing; however, technical information on application is scarce for some areas of knowledge, including irrigation management. The objective of this study is to evaluate an automatic irrigation system with capacitance sensors connected to a local wireless network for the semiautomatic management of irrigation in tomato crops compared with a manual control system based on time-domain reflectometry (TDR)-type sensors. The experiments were carried out in a protected environment, and the seedlings were transplanted following surface drip lines. The study adopted a completely randomized block design consisting of two treatments and 12 repetitions, totaling 24 subplots. The evaluated treatments were an irrigation management system with TDR sensors and a manually-programmed controller, and an irrigation management system with capacitance sensors and a semiautomaticallyprogrammed controller connected to a digital assistant. Quantitative and qualitative parameters as well as the green and dry matter production were evaluated in each treatment. The results indicated that both sensors were effective in managing irrigation in tomato crops. Furthermore, both systems were accurate, and the Alexa® digital assistant was efficient in programming the GreenIQ® semiautomatic system by voice commands.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.