{"id":768,"date":"2021-06-16T08:16:57","date_gmt":"2021-06-16T13:16:57","guid":{"rendered":"https:\/\/centaur.ag\/?p=694"},"modified":"2022-03-08T15:12:29","modified_gmt":"2022-03-08T15:12:29","slug":"early-detection-of-grain-spoilage-using-co2-sensors-and-ai","status":"publish","type":"post","link":"https:\/\/centaur.ag\/pt\/early-detection-of-grain-spoilage-using-co2-sensors-and-ai\/","title":{"rendered":"Gerenciando gr\u00e3os armazenados de forma lucrativa com sensores inteligentes de CO2<\/sub> e IA"},"content":{"rendered":"\r\n

\u00a0It has been established with laboratory and field\u00a0trials that spoiling grain produces higher\u00a0CO2<\/sub>\u00a0levels\u00a0compared to good quality grain. This is usually attributed\u00a0to a combination of factors\u00a0such as the presence of mold or\u00a0insect infestation.\u00a0Grain\u00a0spoilage\u00a0may occur\u00a0in localized areas\u00a0(aka “hotspots”) in a grain store which can be missed by temperature cables installed in\u00a0a silo, making early detection of spoilage difficult. This often causes costly food waste and adversely impacts the environment as well as agricultural income.\u00a0Detecting and\u00a0monitoring CO2<\/sub>\u00a0levels, in conjunction with AI methods,\u00a0offers valuable early warnings about grain spoilage which\u00a0might otherwise go undetected with conventional\u00a0monitoring methods\u00a0[1].\u00a0<\/p>\r\n\r\n\r\n\r\n

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It is now possible to use wireless sensor devices\u00a0to\u00a0measure\u00a0small differences\u00a0of CO2<\/sub>\u00a0in the headspace or\u00a0inside the grain bulk. These differences would be imperceptible to human beings, but\u00a0comparing them to\u00a0atmospheric\u00a0CO2<\/sub>\u00a0concentration and combining them with other sensor data, predictive algorithms can\u00a0detect situations where\u00a0mold\u00a0spoilage is developing\u00a0[2]. This enables growers and grain store managers to take timely corrective action such as aerating, turning,\u00a0selling,\u00a0or fumigating the grain.\u00a0Technology can be used effectively to make grain management easier, more sustainable, and more profitable.<\/p>\r\n\r\n\r\n\r\n

BACKGROUND<\/strong>\u00a0<\/p>\r\n\r\n\r\n\r\n

The global average atmospheric carbon dioxide\u00a0concentration\u00a0is\u00a0approximately 410\u00a0parts per million (ppm for short). When grain is stored in a silo or a warehouse\u00a0the\u00a0main factors that cause the concentration to deviate\u00a0from atmospheric levels\u00a0are\u00a0the\u00a0respiration\u00a0of the\u00a0grain,\u00a0the fermentation of mold,\u00a0and the reproduction of insects.\u00a0In parallel,\u00a0depending on how airtight the storage structure is,\u00a0generated\u00a0CO2<\/sub>\u00a0may escape to\u00a0the ambient environment, causing\u00a0a\u00a0reduction of CO2<\/sub>\u00a0concentration\u00a0(Figure 1).\u00a0<\/p>\r\n\r\n\r\n\r\n

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Figure 1: Schematic of CO2<\/sub> flows in a silo<\/figcaption>\r\n<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

CO<\/strong>2<\/sub><\/strong>\u00a0<\/strong>PRODUCTION IN THE GRAIN BULK<\/strong>\u00a0<\/p>\r\n\r\n\r\n\r\n

A.\u00a0Grain\u00a0Respiration\u00a0<\/p>\r\n\r\n\r\n\r\n

Grain is a living and breathing organism. During\u00a0grain\u00a0respiration, starch and oxygen are converted to carbon dioxide as well as water and heat.\u00a0An increase in storage temperature leads to an increase in the\u00a0respiration\u00a0rate\u00a0and thus the CO2<\/sub>\u00a0levels\u00a0(Figure 2). Nutrients being respired lead to losses in the weight and quality of stored produce\u00a0[3].\u00a0<\/p>\r\n\r\n\r\n\r\n

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Figure 2: Representation of the complete combustion (aerobic respiration) of a typical starch carbohydrate<\/figcaption>\r\n<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

B.\u00a0Mold\u00a0respiration\u00a0<\/p>\r\n\r\n\r\n\r\n

The growth of toxigenic fungi can adversely affect grain quality and even produce mycotoxins of food safety concern,\u00a0which should be sensitively monitored and controlled during grain storage.\u00a0Researchers\u00a0[4] were able to\u00a0establish the relationship between the growth\u00a0of toxigenic fungi and their carbon dioxide\u00a0production. The results showed the CO2<\/sub>\u00a0concentrations increased exponentially during the growth\u00a0of toxigenic fungi Aspergillus flavus<\/em>, Penicillium sp.<\/em> and Aspergillus\u00a0ochraceus<\/em>, which was different from the linear\u00a0increase of CO2<\/sub>\u00a0concentration produced by the non-toxigenic xerophilic fungi Aspergillus\u00a0glaucus<\/em>\u00a0and Aspergillus\u00a0restrictus<\/em>. The acceleration of CO2<\/sub>\u00a0concentration was\u00a0also\u00a0found much earlier than the growth of toxigenic fungi, which\u00a0would be useful for the prevention of grain spoilage\u00a0(Figure 3).\u00a0This rapid increase of CO2<\/sub>\u00a0concentration in stored grains could be considered as an indication of the growth of toxigenic fungi and consequently of greater\u00a0risk of microbial spoilage of grains\u00a0[4].\u00a0\u00a0<\/p>\r\n\r\n\r\n\r\n

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Figure 3:\u00a0Changes in CO2<\/sub>\u00a0concentration (blue) and fungal number (red) of toxigenic fungi A. flavus<\/em>\u00a0grown at 16% moisture content [4].\u00a0The acceleration of CO2<\/sub>\u00a0concentration was found to be a precursor of toxigenic fungi.\u00a0<\/figcaption>\r\n<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

C.\u00a0Insect\u00a0respiration\u00a0<\/p>\r\n\r\n\r\n\r\n

Several scientific studies have used CO2<\/sub>\u00a0concentrations as an indicator of insect\u00a0infestation. More recently, a group of researchers\u00a0[5] determined the\u00a0respiration\u00a0rate of grain insects (S.\u00a0zeamais, R.\u00a0dominica<\/em>,\u00a0and T.\u00a0castaneum<\/em>) at various temperatures.\u00a0All three\u00a0species yielded linear increases in CO2<\/sub>\u00a0concentrations.\u00a0The\u00a0respiration\u00a0rates of R.\u00a0dominica<\/em>\u00a0were lower than S.\u00a0zeamais<\/em>\u00a0and T.\u00a0castaneum<\/em>\u00a0possibly because R.\u00a0dominica<\/em>\u00a0expresses less\u00a0movement than the other two species.\u00a0In the same study, the researchers compared the CO2<\/sub>\u00a0production rates in an insect-free silo and an infested silo\u00a0(filled with paddy rice).\u00a0As seen in Figure\u00a04,\u00a0the CO2<\/sub>\u00a0concentration curve from the\u00a0infested\u00a0silo with higher infestation\u00a0expressed a steeper slope than\u00a0the insect-free silo.\u00a0For those reasons, the researchers concluded that\u00a0monitoring CO2<\/sub>\u00a0concentrations could\u00a0detect insect\u00a0presence\u00a0and\u00a0potentially be an effective tool for\u00a0determining\u00a0insect population density during grain storage.\u00a0<\/p>\r\n\r\n\r\n\r\n

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Figure 4: CO2<\/sub>\u00a0concentration curves in infested and insect-free silos<\/figcaption>\r\n<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

CO<\/strong>2<\/sub><\/strong>\u00a0<\/strong>S<\/strong>ENSOR TECHNOLOGY\u00a0<\/strong>\u00a0<\/p>\r\n\r\n\r\n\r\n

Centaur Internet-of-Crops\u00ae provides advanced grain storage technology which includes wireless, battery-operated sensors (Figure\u00a05). These devices measure accurately all the parameters that affect grain quality (such as\u00a0temperature, relative humidity, O2<\/sub>, and CO2<\/sub>)\u00a0and\u00a0fumigation\u00a0applications (e.g.\u00a0phosphine). They also\u00a0transmit\u00a0safely their data to the cloud in real-time, offering worldwide accessibility. As analyzed above, monitoring gases in a silo offer significant advantages to early\u00a0spoilage\u00a0detection since sensors can detect variations in gas concentrations significantly faster than variations in temperature and relative humidity.\u00a0<\/p>\r\n\r\n\r\n\r\n

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Figure\u00a05: Example of wireless\u00a0CO2<\/sub>\u00a0sensor installation inside a silo<\/figcaption>\r\n<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n

AI-POWERED ANALYTICS OF\u00a0CO2<\/sub><\/strong>\u00a0IN STORED CROP<\/strong>S<\/p>\r\n\r\n\r\n\r\n

Due to the complexity of mechanisms that produce CO2<\/sub>\u00a0in a silo or the way\u00a0it escapes to the ambient, evaluation of the\u00a0CO2<\/sub>\u00a0readings is not\u00a0straightforward.\u00a0Good news is, this now becomes feasible thanks to Machine Learning algorithms.\u00a0Figure\u00a06\u00a0presents the\u00a0CO2<\/sub>\u00a0concentrations of two silo bins as recorded by Centaur sensors. Even though Bin B has higher in-grain\u00a0CO2<\/sub>\u00a0values than Bin A (at least for the first\u00a0few days), the algorithm\u00a0does not\u00a0issue an alert since the grain condition is deemed good. On the contrary, the silo manager of Bin A would receive an alert as early as the 17th<\/sup>\u00a0day since a sudden rise of the\u00a0CO2<\/sub>\u00a0is detected and flagged by the algorithm as an early sign of grain spoilage.<\/p>\r\n\r\n\r\n\r\n

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Figure\u00a06: CO2<\/sub>\u00a0concentrations of two silo bins as recorded by wireless sensors. The silo manager of Bin A would receive an alert as early as from the 17th day since a sudden rise of the CO2<\/sub>\u00a0is detected.\u00a0<\/figcaption>\r\n<\/figure>\r\n\r\n\r\n\r\n

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DATA MANAGEMENT<\/strong>\u00a0<\/p>\r\n\r\n\r\n\r\n

Besides monitoring\u00a0CO2<\/sub>\u00a0concentration for grain quality control purposes, it\u2019s also necessary to present data to grain managers or customers in a meaningful and user-friendly way.\u00a0As part of its Internet-of-Crops\u00ae system, Centaur has developed a\u00a0web platform\u00a0where all the sensor data is automatically\u00a0processed\u00a0and users can receive automated notifications if abnormal behavior is detected.\u00a0The data in the web platform are updated in real-time, 24\/7 and users could access the information from their smart devices at any place.\u00a0Connecting the system with smart actuators (such as aeration fan relays) is also possible.<\/p>\r\n\r\n\r\n\r\n

Interested in a demo of the Internet-of-Crops\u00ae user interface? Contact us<\/strong><\/a>.<\/p>\r\n\r\n\r\n\r\n

CONCLUSIONS<\/strong><\/p>\r\n\r\n\r\n\r\n

CO2<\/sub>\u00a0monitoring and\u00a0analysis\u00a0can significantly improve grain quality and bring unprecedented\u00a0benefits for\u00a0its adopters:\u00a0<\/p>\r\n\r\n\r\n\r\n