Optical detection of single and multiple seeding using an innovative shape-recognition algorithm

• Published in: Smart agricultural technology Vol. 9; p. 100652
• Main Authors: Ghaffarnezhad, Ali, Navid, Hossein, Karimi, Hadi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2024; Elsevier
• Subjects: IR sensor; Precision planter; Recognition algorithm; Seed counting; Seed sensor; IR sensor; Precision planter; Seed sensor; Recognition algorithm; Seed counting
• ISSN: 2772-3755; 2772-3755
• DOI: 10.1016/j.atech.2024.100652

•A novel dust-resistant sensing system was designed specifically for precision seeding, enabling accurate seed counting.•An innovative algorithm was developed to effectively process and count passing seeds by analyzing their shadow shapes.•The newly developed algorithm demonstrates excellent accuracy in single and multiple seed counting, irrespective of seed type or size, eliminating the need for individualized settings for each seed.•Through testing on the precision planter platform, the sensor consistently achieved an accuracy rate exceeding 97%. The ability to accurately and continuously monitor seeding quality allows farmers to improve their seeding practices, maintain appropriate seed spacing, and maximize crop productivity. The precise detection and counting of individual seeds, especially those that pass through and overlap in pairs or multiples, is a major challenge in developing seed monitoring systems. In this research, a novel shape-based algorithm is presented that utilizes seven pairs of 3-mm infrared LEDs to efficiently recognize and count both single seeds and overlapping seeds. The outputs of the receivers were converted to binary signals by comparing them to samples taken when no seed flow was present. Each infrared receiver acted as a pixel, and to recognize and count multiple seeds, their outputs were temporarily stored in a matrix with seven columns. When the seeds passed the sensor, their outputs were analyzed together using an innovative and systematic clustering algorithm. This enables shape-based processing and recognition of passing seeds regardless of type and size, effectively reducing counting errors. The developed system was subjected to testing using seven different seed types on a conveyor belt, while one seed type underwent testing on a precision corn seeder in the laboratory. The results indicated a high level of agreement between the sensor data and the actual seed rates. The average counting accuracy for popcorn, hybrid corn, chickpea, pinto beans, coated tomato, mung beans, and soybean seeds on the conveyor belt platform was 0.99, 0.99, 0.98, 0.96, 0.8, 0.99, and 0.99, respectively. The results showed that the accuracy of the algorithm increases with increasing sphericity and size of the seeds relative to the distance between the adjacent LEDs. Moreover, the counting accuracy exceeded 97% for all rates on the precision corn seeder platform, with an average accuracy of 0.986.