FPGA Implementation of a Convolutional Neural Network and Its Application for Pollen Detection upon Entrance to the Beehive

Tomyslav Sledevič, Artūras Serackis and Darius Plonis ()
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Tomyslav Sledevič: Department of Electronic Systems, Vilnius Gediminas Technical University, Saulėtekio Ave. 11, LT-10223 Vilnius, Lithuania
Artūras Serackis: Department of Electronic Systems, Vilnius Gediminas Technical University, Saulėtekio Ave. 11, LT-10223 Vilnius, Lithuania
Darius Plonis: Department of Electronic Systems, Vilnius Gediminas Technical University, Saulėtekio Ave. 11, LT-10223 Vilnius, Lithuania

Agriculture, 2022, vol. 12, issue 11, 1-17

Abstract: The condition of a bee colony can be predicted by monitoring bees upon hive entrance. The presence of pollen grains gives beekeepers significant information about the well-being of the bee colony in a non-invasive way. This paper presents a field-programmable-gate-array (FPGA)-based pollen detector from images obtained at the hive entrance. The image dataset was acquired at native entrance ramps from six different hives. To evaluate and demonstrate the performance of the system, various densities of convolutional neural networks (CNNs) were trained and tested to find those suitable for pollen grain detection at the chosen image resolution. We propose a new CNN accelerator architecture that places a pre-trained CNN on an SoC FPGA. The CNN accelerator was implemented on a cost-optimized Z-7020 FPGA with 16-bit fixed-point operations. The kernel binarization and merging with the batch normalization layer were applied to reduce the number of DSPs in the multi-channel convolutional core. The estimated average performance was 32 GOPS for a single convolutional core. We found that the CNN with four convolutional and two dense layers gave a 92% classification accuracy, and it matched those declared for state-of-the-art methods. It took 8.8 ms to classify a 512 × 128 px frame and 2.4 ms for a 256 × 64 px frame. The frame rate of the proposed method outperformed the speed of known pollen detectors. The developed pollen detector is cost effective and can be used as a real-time image classification module for hive status monitoring.

Keywords: convolutional neural network (CNN); field-programmable gate array (FPGA); pollen detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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