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Horticulturae
Volume 11
Issue 7
10.3390/horticulturae11070718
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Predicting Sweet Pepper Yield Based on Fruit Counts at Multiple Ripeness Stages Monitored by an AI-Based System Mounted on a Pipe-Rail Trolley †

by
Kota Shimomoto
1,*,
Mitsuyoshi Shimazu
1,
Takafumi Matsuo
2,
Syuji Kato
2,
Hiroki Naito
3,
Masakazu Kashino
1,
Nozomu Ohta
1,
Sota Yoshida
1 and
Tokihiro Fukatsu
1
1
Institute of Agricultural Machinery, National Agriculture and Food Research Organization, Tsukuba 3050856, Japan
2
Takahiko Agro-Business Co., Ltd., Kokonoe 8794802, Japan
3
Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 1138657, Japan
*
Author to whom correspondence should be addressed.
†
The findings presented in this manuscript were partially reported in proceedings of the International Symposium on New Technologies for Sustainable Greenhouse Systems: GreenSys2023, Cancún (Mexico), 22–27 October 2023, Available online: https://www.ishs.org/ishs-article/1426_49.
Horticulturae 2025, 11(7), 718; https://doi.org/10.3390/horticulturae11070718
Submission received: 19 May 2025 / Revised: 15 June 2025 / Accepted: 19 June 2025 / Published: 20 June 2025
(This article belongs to the Special Issue Artificial Intelligence in Horticulture Production)
Versions Notes

Abstract

In our previous study, we developed a monitoring system for automatically counting tomatoes produced in protected horticulture using deep learning–based object detection. In this study, we adapted the system for sweet peppers and developed a monitoring system tailored to this crop. We evaluated its fruit detection and counting performance in a large-scale commercial greenhouse. Furthermore, we investigated the relationship between fruit counts at different ripeness stages and the total yield in the cultivation area, and we assessed the accuracy when predicting the yield for the following week. The results confirmed that the system maintained a stable fruit detection performance throughout the trial, and that its outputs were reliable enough to indicate its potential to replace manual counting. In addition, the average number of fruits at the 1–40% and 41–80% ripeness stages across six planting rows showed a correlation with the total weekly yield in the entire 0.6 ha cultivation area the following week. A yield prediction model using average fruit counts at these two ripeness stages as explanatory variables achieved a WAPE of 21.35%, indicating that the monitoring system is effective for yield prediction.
Keywords: fruit detection; maturity classification; continuous monitoring; harvest timing; large-scale greenhouse fruit detection; maturity classification; continuous monitoring; harvest timing; large-scale greenhouse

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MDPI and ACS Style

Shimomoto, K.; Shimazu, M.; Matsuo, T.; Kato, S.; Naito, H.; Kashino, M.; Ohta, N.; Yoshida, S.; Fukatsu, T. Predicting Sweet Pepper Yield Based on Fruit Counts at Multiple Ripeness Stages Monitored by an AI-Based System Mounted on a Pipe-Rail Trolley. Horticulturae 2025, 11, 718. https://doi.org/10.3390/horticulturae11070718

AMA Style

Shimomoto K, Shimazu M, Matsuo T, Kato S, Naito H, Kashino M, Ohta N, Yoshida S, Fukatsu T. Predicting Sweet Pepper Yield Based on Fruit Counts at Multiple Ripeness Stages Monitored by an AI-Based System Mounted on a Pipe-Rail Trolley. Horticulturae. 2025; 11(7):718. https://doi.org/10.3390/horticulturae11070718

Chicago/Turabian Style

Shimomoto, Kota, Mitsuyoshi Shimazu, Takafumi Matsuo, Syuji Kato, Hiroki Naito, Masakazu Kashino, Nozomu Ohta, Sota Yoshida, and Tokihiro Fukatsu. 2025. "Predicting Sweet Pepper Yield Based on Fruit Counts at Multiple Ripeness Stages Monitored by an AI-Based System Mounted on a Pipe-Rail Trolley" Horticulturae 11, no. 7: 718. https://doi.org/10.3390/horticulturae11070718

APA Style

Shimomoto, K., Shimazu, M., Matsuo, T., Kato, S., Naito, H., Kashino, M., Ohta, N., Yoshida, S., & Fukatsu, T. (2025). Predicting Sweet Pepper Yield Based on Fruit Counts at Multiple Ripeness Stages Monitored by an AI-Based System Mounted on a Pipe-Rail Trolley. Horticulturae, 11(7), 718. https://doi.org/10.3390/horticulturae11070718

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