{"id":134958,"date":"2026-10-27T22:49:33","date_gmt":"2026-10-27T22:49:33","guid":{"rendered":"https:\/\/www.seeedstudio.com\/blog\/?p=134958"},"modified":"2026-10-27T23:15:33","modified_gmt":"2026-10-27T23:15:33","slug":"from-experience-to-evidence-how-growers-in-ningxia-use-sensecap-sensors-to-help-prevent-cherry-tomato-cracking","status":"publish","type":"post","link":"https:\/\/www.seeedstudio.com\/blog\/2026\/10\/27\/from-experience-to-evidence-how-growers-in-ningxia-use-sensecap-sensors-to-help-prevent-cherry-tomato-cracking\/","title":{"rendered":"From Experience to Evidence: How Growers in Ningxia Use SenseCAP Sensors to Help Prevent Cherry Tomato Cracking"},"content":{"rendered":"\t\t
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Across two growing areas, 18 sensing devices helped turn 40 days of environmental observations into daily risk assessments and practical growing decisions.<\/em><\/p>\n

In protected cultivation, few quality problems are as frustrating as cherry tomato cracking.<\/p>\n

A run of overcast, rainy days followed by sudden sunshine can quickly turn a promising harvest into a sorting challenge. Why did the fruit crack? Which greenhouse is most at risk? What should the team do tomorrow?<\/p>\n

Traditionally, the answers depended heavily on experienced growers walking through the greenhouses\u2014watching the weather, feeling the fruit, and checking the soil underfoot. That expertise was valuable, but much of it remained in people\u2019s heads: difficult to document, share, or turn into an early warning.<\/p>\n

During the first harvest season of 2026, the Haifan project covered approximately 500 mu, or 33 hectares, across Zhongning and Haiyuan in Ningxia, China. At these sites, environmental monitoring became part of the growing teams\u2019 daily efforts to anticipate cracking risks and take preventive action.<\/p>\n

The project combined Seeed Studio\u2019s SenseCAP Combo 4G Environmental Sensor<\/a><\/em><\/strong> and SenseCAP S700-C 7-in-1 Compact Weather Station with radar rainfall sensing <\/a><\/em><\/strong>(used with SenseCAP Senor Hub 4G Data Logger<\/em><\/strong><\/a>). An AI agent platform called OpenClaw analyzed the environmental observations and supported daily risk assessments.<\/p>\n

Together, the sensors and platform helped the teams move from reviewing cracking after it occurred toward identifying risks and planning preventive action before the next harvest.<\/p>\n

What did the system reveal over those 40 days? And how did those observations change everyday decisions in the greenhouse?<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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1. A Digital Nervous System in the Field<\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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This was not simply a matter of buying a few sensors and hanging them in greenhouses.<\/p>\n

The project established a connected workflow: sensing => analysis => action, supported by continuous environmental observations.<\/p>\n

Across the two growing areas, 18 SenseCAP sensing devices were deployed in a range of structures, including high-arched plastic houses, normal-arched houses, old houses without ventilation openings, and multi-span greenhouses across multiple sites including Gaiwan, Luoshan, Gaotai etc..<\/p>\n

Quick notes (4 types of environments)<\/i><\/p>\n