{"id":1328,"date":"2026-09-23T17:16:46","date_gmt":"2026-09-23T09:16:46","guid":{"rendered":"https:\/\/durianjournal.com\/sg\/?p=1328"},"modified":"2026-09-23T17:16:46","modified_gmt":"2026-09-23T09:16:46","slug":"how-ai-sensors-monitor-cold-storage-durian-quality","status":"publish","type":"post","link":"https:\/\/durianjournal.com\/sg\/how-ai-sensors-monitor-cold-storage-durian-quality\/","title":{"rendered":"How AI Sensors Monitor Cold Storage Durian Quality"},"content":{"rendered":"<style>@import url('https:\/\/fonts.googleapis.com\/css2?family=Merriweather:ital,wght@0,100..900;1,100..700;1,100..900&family=Montserrat:ital,wght@0,100..900;1,100..900&family=Roboto:ital,wght@0,100..900;1,100..900&family=Rubik:ital,wght@0,100..900;1,100..900&display=auto');.wp-premium-body { font-family: 'Rubik', 'Roboto', sans-serif; color: #333333; line-height: 1.75; font-size: 16px; margin: 0 auto; }.wp-premium-callout { font-family: 'Merriweather', serif; background-color: #f4f7f6; border-left: 4px solid #00a88f; padding: 22px; margin: 25px 0; border-radius: 0 8px 8px 0; }.wp-premium-cta-box { background: linear-gradient(135deg, #111111 0%, #2c3e50 100%); color: #ffffff; padding: 40px; text-align: center; border-radius: 12px; margin: 40px 0; }.wp-premium-btn { display: inline-block; background-color: #00a88f; color: #ffffff !important; padding: 14px 35px; text-decoration: none !important; font-weight: bold; border-radius: 5px; margin-top: 15px; }<\/style>\n<div class='wp-premium-body'>\n<div class='wp-premium-callout'><strong>Quick Summary:<\/strong><\/p>\n<p>Cold storage durian quality control in Klang Valley no longer relies on a worker pressing a thumb into the husk every three hours. Instead, a paired stack of ethylene MOS sensors, LWIR cameras, and NIR hyperspectral imagers feeds a small edge inference unit that classifies ripeness and triggers cooling zone adjustments inside the warehouse. This article walks through the five operational phases of that monitoring loop.<\/p>\n<\/div>\n<h2>Step 1: Attach Ethylene and CO2 Sensor Arrays<\/h2>\n<p>Durian metabolizes even at the recommended storage window of 5\u20137\u00b0C. The fruit\u2019s postharvest respiration shifts the internal gas balance, and the first reliable indicator of premature ripening is a spike in ethylene. In a working setup at a Shah Alam cold storage facility, the array typically uses <strong>Figaro TGS2602-B<\/strong> metal-oxide gas sensors mounted on each pallet\u2019s ventilation duct. These sensors detect ethylene from 0.05 ppm upward. Next to them, a <strong>Sensirion SCD30<\/strong> CO2 sensor tracks respiration rate.<\/p>\n<p>The mounting position matters. Sensors placed on the return-air side of the evaporator unit read bulk cycles, not per-pallet conditions. For per-pallet reading, the sensor board must sit at the point where air exits the pallet\u2019s bottom vent core. Malaysian operators who skip this detail end up measuring the cold room, not the fruit. Wiring runs through CPVC conduit because the acidic skin compounds corrode standard PVC. Calibration is done against a 10 ppm ethylene gas cylinder before each Musang King season batch.<\/p>\n<h2>Step 2: Scan Pallets with Thermal and Near-Infrared Cameras<\/h2>\n<p>Gas sensors tell you <em>that<\/em> the fruit is ripening. Thermal and NIR imaging tell you <em>where<\/em> on the pallet and <em>how far along<\/em> the process has progressed.<\/p>\n<p>A <strong>FLIR A400 LWIR camera<\/strong>, mounted on the gantry above the conveyor belt between the night loading dock and the cold room entrance, captures a thermal map of every pallet surface. Durian pulp temperature stays around 6.5\u00b0C when stable; any area above 8.2\u00b0C suggests poor airflow bypass or a gap in the pallet wrap. The camera runs at 60 fps with a sensitivity of \u00b10.5\u00b0C. Because the warehouse floor in Puchong has thermal drift from afternoon sun hitting the dock door, the camera\u2019s auto-focus is disabled. Instead, the image acquisition script performs a fixed-focus capture triggered by the pallet\u2019s optical proximity sensor.<\/p>\n<p>Then comes the <strong>Specim FX17 hyperspectral imager<\/strong> in the 900\u20131700 nm range. It scans the husk\u2019s reflectance. The spectral signature at 1150 nm and 1450 nm correlates well with interior sugar density and moisture of the pulp\u2014this has been validated on MD2 and Musang King samples. But there is a catch: the FX17 sees the surface, not the inside. The model compensates using a known transfer function derived from destructive tests of 300 fruit across three Malay Peninsula farms.<\/p>\n<h2>Step 3: Run Edge Inference on Grading Models<\/h2>\n<p>All of this sensor output lands on an edge device\u2014typically a <strong>Coral Edge TPU<\/strong> for classification or a <strong>NVIDIA Jetson Orin Nano<\/strong> when the deployment also runs the NIR model. In most KL setups, the Jetson is preferred because it can execute both the thermal-image mask and the spectral classifier without sending data to a cloud backend.<\/p>\n<p>The inference pipeline runs in five stages:<\/p>\n<p>1. Normalize thermal frames using a per-pallet emissivity correction for green vs. brown husk surfaces.<\/p>\n<p>2. Extract average ethylene and CO2 slope over the last 30 minutes.<\/p>\n<p>3. Feed the spectral reflectance vector into a LightGBM classifier trained on the destructive-test dataset.<\/p>\n<p>4. Combine the two outputs using a rule: if ethylene slope exceeds 0.3 ppm\/hour <em>and<\/em> thermal variance above 1.1\u00b0C, the pallet is flagged as \u201centering climacteric phase.\u201d<\/p>\n<p>5. Emit a grade score from 0.85 to 0.20, where 0.85 means \u201cfit for overnight delivery,\u201d and anything below 0.45 triggers a re-grade.<\/p>\n<p>The latency stays under 50 ms. No cloud, no 4G dependence. The warehouse operates a diesel backup generator anyway because Tenaga Nasional feeder lines in that industrial lane are unreliable during the monsoon.<\/p>\n<h2>Step 4: Adjust Cold Room Fans and Compressors Automatically<\/h2>\n<p>The output of the edge model connects to the cold room\u2019s programmable logic controller via Modbus TCP. When the system detects a warm spot near one evaporator\u2019s airflow shadow, it opens the corresponding variable air volume damper by 15% and raises that fan\u2019s speed from 800 rpm to 1,100 rpm. This resolves the thermal imbalance without forcing the main compressor to cycle.<\/p>\n<p>In the durian-specific case, ethylene is not just a signal\u2014it is a catalyst. One ripening pallet can trigger its neighbors. So the Step 3 classifier also calculates a \u201cspillover risk index.\u201d When that index crosses a threshold, the system commands a 2-minute burst of the ammonia refrigeration compressor to drop the room from 6\u00b0C to 4.5\u00b0C. The low setting is a one-off maneuver; holding it there would damage the pulp\u2019s cell walls and cause candy-like transparency in the flesh.<\/p>\n<p>Malaysian installations need a robust control loop. The typical cold room control board in Shah Alam runs a direct expansion system with hot-gas defrost every 6 hours. The edge unit must suppress the AI trigger during those defrost windows, otherwise the temperature rise from the defrost cycle gets misread as a ripening event.<\/p>\n<h2>Step 5: Tag Batches with Predicted Pulp Shelf-Life<\/h2>\n<p>The final phase is output labeling. Every pallet gets a Grade A, B, or C sticker printed by a barcode printer at the chiller\u2019s exit. The grade is not just visual\u2014it embeds the model\u2019s predicted remaining shelf life at 6\u00b0C.<\/p>\n<p>&#8211; <strong>Grade A<\/strong> (sensor score 0.70+): ripening suppressed, predicted shelf life 14\u201316 days. Good for West Malaysia distribution.<\/p>\n<p>&#8211; <strong>Grade B<\/strong> (0.45\u20130.69): mild climacteric activity; shelf life 8\u201312 days. Marked for immediate transfer to the Jalan Ipoh wholesale market tier.<\/p>\n<p>&#8211; <strong>Grade C<\/strong> (below 0.45): high ethylene and internal fermentation signature; shelf life under 6 days. Redirected to a pulp-processing partner in Puchong that makes frozen durian puree.<\/p>\n<p>The shelf-life tag is printed in both English and Malay, with the digital twin record stored in a structured query language table on the warehouse local area network. This gives the quality audit trail that the Malaysian Department of Standards\u2019 MS 1501 fresh-fruit classification expects. Once the pallet reaches a retail buyer in SS15, the buyer can scan the QR code to see the original spectral curve\u2014not a hand-written \u201cchecked on\u201d date.<\/p>\n<div style='overflow-x: auto; margin: 30px 0;'>\n<table class='wp-premium-table'>\n<thead>\n<tr>\n<th>Sensor Array<\/th>\n<th>Key Measurement<\/th>\n<th>Quality Metric It Predicts<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>&#8212;<\/td>\n<td>&#8212;<\/td>\n<td>&#8212;<\/td>\n<\/tr>\n<tr>\n<td>Figaro TGS2602-B (metal-oxide)<\/td>\n<td>0.05\u201310 ppm ethylene<\/td>\n<td>Onset of climacteric phase<\/td>\n<\/tr>\n<tr>\n<td>Sensirion SCD30 + Vaisala HMP110<\/td>\n<td>CO2 and relative humidity<\/td>\n<td>Fermentation risk and stale-air pockets<\/td>\n<\/tr>\n<tr>\n<td>FLIR A400 LWIR camera<\/td>\n<td>Surface temperature map \u00b10.5\u00b0C<\/td>\n<td>Airflow shadows and evaporator bypass<\/td>\n<\/tr>\n<tr>\n<td>Specim FX17 NIR hyperspectral imager<\/td>\n<td>900\u20131700 nm reflectance indices<\/td>\n<td>Pulp sugar density and interior moisture<\/td>\n<\/tr>\n<tr>\n<td>Coral Edge TPU \/ Jetson Orin Nano<\/td>\n<td><50 ms on-prem inference<\/td>\n<td>Real-time pallet grade score (A\/B\/C)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<div class='wp-premium-cta-box'>\n<h3>Ready to Accelerate Your Digital Growth Strategy?<\/h3>\n<p>Partner with an industry-leading digital agency to upscale your infrastructure today.<\/p>\n<p><a href='https:\/\/www.justsimple.com.my\/' class='wp-premium-btn'>Get Started for Free Today<\/a><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Quick Summary: Cold storage durian quality control in Klang Valley no longer relies on a worker pressing a thumb into the husk every three hours. Instead, a paired stack of ethylene MOS sensors, LWIR cameras, and NIR hyperspectral imagers feeds a small edge inference unit that classifies ripeness and triggers cooling zone adjustments inside the [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":1396,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_ppma_block_editor_authors":""},"categories":[15],"tags":[],"ppma_author":[36],"class_list":["post-1328","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-lifestyle-culture"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>How AI Sensors Monitor Cold Storage Durian Quality - Durian Journal Singapore<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/durianjournal.com\/sg\/how-ai-sensors-monitor-cold-storage-durian-quality\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How AI Sensors Monitor Cold Storage Durian Quality - Durian Journal Singapore\" \/>\n<meta property=\"og:description\" content=\"Quick Summary: Cold storage durian quality control in Klang Valley no longer relies on a worker pressing a thumb into the husk every three hours. 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