ABSTRACT Agriculture is one of the segments that most uses water and developments have been made to save irrigation water. Deficit irrigation is a technique that can contribute to production and water saving in agriculture. This study aimed to evaluate the viability of deficit irrigation in tomato production irrigated by subsurface drip in a greenhouse and estimate water saving. The experiment was conducted at the CCA/UFSCar, in Araras, São Paulo, Brazil, with grape tomato cultivation. It consisted of three treatments, 100 % water depth and deficit irrigation (75 and 50 % of water depth), with a randomized block design. Irrigation management was performed using mean soil moisture data collected through TDR probes installed in each treatment. Tomato plants were cultivated for 137 days and conducted vertically with one stem and six bunches. Fruit size, number and mass of fruits per plant, fruit pH and soluble solids were attributes measured and analyzed weekly. The deficit irrigation of 50 % treatment presented lower values in all attributes evaluated and 90.6 % of water saving. The 75 % treatment showed lower value only for pH and fruit diameter and 70.4 % of water saving. Deficit irrigation of 75 % was viable for tomato cultivation in greenhouse and for water saving in crop cycle.

ABSTRACT 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 semiautomatically-programmed 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.