Automatic acoustical surveillance system of grains in silos.

The focus of this task was to develop a novel detection system utilising incidental acoustic signal of grain insects at their active developmental stages (larva or adult stage). The system should be able to automatically determine what type of insect is present through the processing of the specific sound it produces when moving or feeding. This was achieved in combining a reliable transducer or insect noise spectra (a piezo-electric acoustical sensor) to an artificial intelligence tool enabling specific sound signature recognition. The acoustical probe was created from the technology of acoustical detection of small insects already developed through previous INRA works (Andrieu and Fleurat-Lessard, 1986; 1990; Fleurat-Lessard et al., 1994), that resulted in a very high signal-noise-ratio system. The probe equipped with two sensors was designed in order to be introduced in the grain bulk from the surface to a depth of 2.5 m maximum. The probe can be linked to a data logger for the direct recording (analogic signal) acoustical activity of insects inside the grain bulk. A classification system was designed for the ayutomatic identification of either insect stage (larva or adult) or the insect family or species (weevils, lesser grain borer, flour beetles, and saw toothed grain beetle). This classification was achieved through the creation of a computer program that made profiles of different sounds encountered when searching for infestation in grain bulks. A reference database of insect sounds was constituted. For each studied species, a specific profile of sounds was computed from average of 50 to several hundreds pulses collected under controlled infestation. The raw signal processing was performed in two steps: (i) the reduction and removing of the stationary ambient noise, and (ii) the treatment of the remaining transient noises. Then, a classification algorithm was used to extract the features allowing (i) the automatic discrimination between insect noise, non-insect noise, insect stage (larva or adult), and (ii) the quantitation of true insect activity (for density calculation, eventually). A likelihood coefficient was calculated from calibrated detection experiments in laboratory conditions. For the first discriminating step larva/adult noise discrimination), the correct identification of the stage was between 68 and 73 %. For the second discriminating step (the four groups of grain insect species cited above), the percentages of correctly identified groups ranged from 60.8 to 72.6 % and from 52.4 to 72.8 %, for the adult stage and the larval stage, respectively. For practical use, a man-machine interface was defined with the participation of experts and users' representatives. The validation was achieved in pilot storage conditions with a known density of infestation by a single species in a small bin of malting barley grain. With weevil infestation, a density of adults as low as 1 insect per 25 kg could be detected. Other species at the adult stage and the weevil larvae were detected at density levels less than 1 insect per 5 kg malting barley grain, except for the saw-toothed grain beetle.