By integrating disease detection and fruit counting in real time, this innovation promises to enhance sustainable production practices for one of the world’s most vital crops.
Tomatoes are a critical source of nutrients and remain one of the most widely cultivated fruits globally. However, intensive greenhouse practices increase susceptibility to diseases, which can reduce yields by up to 30% and degrade fruit quality. Traditional methods of plant monitoring are labor-intensive and often ineffective in real-world conditions due to environmental challenges like varying lighting and background complexity.
A study (DOI:10.34133/plantphenomics.0174) published in Plant Phenomics on 15 April 2024, revolutionizes greenhouse management and prevents disease spread.
The research utilized YOLOX, NanoDet, and YOLO-TGI algorithms to evaluate the accuracy of tomato disease detection and fruit counting. Various detectors were tested on a comprehensive dataset, and confidence distributions for unhealthy leaves, healthy leaves, and tomatoes were visualized using violin plots. Results showed that increased network complexity generally improved detection accuracy. Specifically, YOLOX-M achieved optimal performance, with leaf detection confidence scores concentrated around 0.9, while YOLOX-N demonstrated weaker performance, with scores ranging from 0.2 to 0.7, likely due to its lightweight structure. Both NanoDet and YOLO-TGI displayed similar trends, maintaining robust scores above 0.4. The YOLOX model had the fastest inference speed at 32.35 ms and the highest mean average precision (mAP) of 0.85, while NanoDet featured the smallest model size, and YOLO-TGI achieved the lowest FLOPs and checkpoint weights. For fruit tracking, the YOLO-TGI-S with Byte-Track combination delivered superior performance, with an R² of 0.93 and an RMSE of 9.17. In contrast, NanoDet-S and FairMot recorded the lowest R² of 0.34. Challenges included occlusions, environmental lighting variations, and ID-switching in video streams, mitigated using an ID-filtering strategy. Overall, YOLO-TGI-S paired with Byte-Track emerged as the most effective configuration, offering a high-speed and accurate solution for real-time monitoring of tomato plant growth and yield estimation.
According to the study’s lead researcher, Dr. Shangpeng Sun, “Early detection of diseases and accurate yield estimation are pivotal for sustainable agriculture. Our integrated system represents a significant step forward, offering high-speed and precise monitoring that can be adapted to multiple crops beyond tomatoes.”
This research marks a breakthrough in automated agricultural monitoring. By offering a scalable, efficient, and high-speed solution for disease detection and yield assessment, the system has the potential to transform modern farming practices and support global food security efforts.
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References
DOI
10.34133/plantphenomics.0174
Original Source URL
https://spj.science.org/doi/10.34133/plantphenomics.0174
Funding information
This research was partially supported by the National Key Research and Development Program of China (2022YFD2100601), the Key Research and Development Program of Jiangsu Province (BE2021379), the Agricultural Independent Innovation of Jiangsu Province (CX225009), the National Natural Science Foundation of China (32102081), and Fonds de Recherche du Québec Nature et technologies (FRQNT) Programme de recherche en partenariat—Agriculture durable (grant no. G259806 FRQ-NT 322853 X-Coded 259432). R.K. extends his appreciation for the scholarship provided by CSC and the fund from 333 High Levels Talents Cultivation of Jiangsu Province.
About Plant Phenomics
Science Partner Journal Plant Phenomics is an online-only Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and distributed by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal’s Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.
The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics.
