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Tomato (Solanum lycopersicum L., family Solanaceae) represents one of the most cultivated horticultural crops worldwide. It contains various nutrients, providing health benefits. For better crop management strategies, especially with global warming, it is crucial to investigate how varieties or cultivars bred for different purposes respond to the changing macro or microenvironmental conditions. This research aimed to assess the growth, reproductive development and selected quality parameters of two tomato varieties, 'Sylviana' (a greenhouse hybrid) and 'Bolseno' (a dual-purpose variety), under a greenhouse environment. The study was conducted in the mid-country wet zone of Sri Lanka where the intensive-control greenhouse (T1) maintained a favourable air temperature (<33°C) for crop growth compared to high daytime temperatures of semi-intensive (T2) and less-intensive (T3) greenhouses (up to 36°C). In 'Bolseno', vegetative growth parameters were significantly affected (P<0.05) by different environments but not in 'Sylviana' (P>0.05). T1 resulted in the highest mean fruit weight and mean fruit diameter in both varieties. Since 'Sylviana' performed well in all three growing environments, the fruit quality was assessed by measuring the antioxidant activity (AOX) and total phenolic content (TPC) using 1, 1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging assay and Folin-Ciocalteu method, respectively. Significantly higher AOX (P<0.05) was reported in T1 while T2 had a higher TPC. The results revealed that different tomato genotypes respond differently to changes in micro environmental conditions in terms of growth and some reproductive traits, and the same variety grown in different conditions had different TPC and AOX. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) applications in smart agricultural systems vary from monitoring climate conditions, automating irrigation systems, greenhouse automation, crop monitoring and management, and crop prediction, up to end-to-end autonomous farm management systems. One of the main challenges to the advancement of IoT systems for the agricultural domain is the lack of training data under operational environmental conditions. Most of the current designs are based on simulations and artificially generated data. Therefore, the essential first step is studying and understanding the finely tuned and highly sensitive mechanism plants have developed to sense, respond, and adapt to changes in their environment, and their behavior under field and controlled systems. Therefore, this study was designed to achieve two specific objectives; to develop low-cost IoT components from basic building blocks, and to study the performance of the developed systems, and generate real-time experimental data, with and without plants. Low-cost IoT devices developed locally were used to convert existing basic polytunnels to semi-controlled and monitoring-only polytunnels. Their performances were analyzed and compared with each other based on several matrices while maintaining the planted tomato variety and agronomic practices similar. The developed system performed as expected suggesting the possibility of commercial applications and research purposes. [ABSTRACT FROM AUTHOR]