Improving the seed detection accuracy of piezoelectric impact sensors for precision seeders. Part I: A comparative study of signal processing algorithms

Rossi S., Rubio Scola I., Bourges G., Šarauskis E., KARAYEL D.

Computers and Electronics in Agriculture, cilt.215, 2023 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 215
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.compag.2023.108449
  • Dergi Adı: Computers and Electronics in Agriculture
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, BIOSIS, CAB Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, Food Science & Technology Abstracts, INSPEC, Metadex, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Piezoelectric sensor, Seed flow, Seeder, Seeding performance monitoring, Signal processing algorithms
  • Akdeniz Üniversitesi Adresli: Evet

Özet

One of the crucial issues in seeders and grain drills is measuring the seed flow rate and seeding quality during the seeding operation. An impact sensor based on a piezoelectric sensor was designed and tested for different seeds to detect seeding quality rapidly and accurately in precision seeders. This sensor enables faster and more cost-effective seeding quality determination than grease belt test benches. Seed impact vibrations were detected using a piezoelectric microphone attached to the plate, while seed trajectories were simultaneously recorded with a high-speed camera. The novelty of this work lies in the implementation of a new algorithm called Variable Threshold Peak Detection with Automatic Calculation of Minimum Threshold (VTPD-AM), which enables more efficient signal analysis. The seed detection accuracy of VTPD-AM was comprehensively compared with the literature, specifically the works of Palshikar et al. (2009), Scholkmann et al. (2012), and Ozbek et al. (2014). The performance of the tested algorithms was compared using corn, soybean, and sunflower seeds at simulated forward speeds of 6 and 12 km/h. Based on the experimental results, the algorithm presented in this work (VTPD-AM) and the algorithm developed by Ozbek et al. (2014) achieved the highest seed monitoring accuracy (over 97 %). The advantage of VTPD-AM is that it can be implemented on microcontrollers for real-time applications. In all cases, the percentage of undetected seeds increased with the increase in the seed flow rate.