2022
DOI: 10.1038/s41598-022-12732-1
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Online recognition and yield estimation of tomato in plant factory based on YOLOv3

Abstract: In order to realize the intelligent online yield estimation of tomato in the plant factory with artificial lighting (PFAL), a recognition method of tomato red fruit and green fruit based on improved yolov3 deep learning model was proposed to count and estimate tomato fruit yield under natural growth state. According to the planting environment and facility conditions of tomato plants, a computer vision system for fruit counting and yield estimation was designed and the new position loss function was based on t… Show more

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Cited by 20 publications
(14 citation statements)
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“…Dwarfed, high yielding varieties already exist and have been demonstrated to grow aboard the ISS, and there are extensive and well-characterised collections of diverse tomato cultivars. [90][91][92] For any target species, high-throughput phenotyping will help to identify the varieties with the best yield/size ratio and monitor the production, which would be a first step towards culture automation. 93,94 Modern gene editing and genetic modification technologies will also contribute to developing crops suitable for SpaceAg.…”
Section: Approaches To Creating and Improving Plants For Spacementioning
confidence: 99%
“…Dwarfed, high yielding varieties already exist and have been demonstrated to grow aboard the ISS, and there are extensive and well-characterised collections of diverse tomato cultivars. [90][91][92] For any target species, high-throughput phenotyping will help to identify the varieties with the best yield/size ratio and monitor the production, which would be a first step towards culture automation. 93,94 Modern gene editing and genetic modification technologies will also contribute to developing crops suitable for SpaceAg.…”
Section: Approaches To Creating and Improving Plants For Spacementioning
confidence: 99%
“…99.3% using an improved YOLOv 3 model for online identi cation and yield estimation of tomato fruits in a PF environment. However, the YOLOv 3 model they used was large-scale, making it di cult to apply to lightweight scenarios [24]. The aim of this study is to improve detection accuracy while keeping the model lightweight.…”
Section: Introductionmentioning
confidence: 99%
“…Despite its lightness, the size of the lightweight model was still 46.7 MB. Wang et al [ 33 ] utilized an improved YOLOv3 model for the online recognition and yield estimation of tomato fruits in a PF environment, achieving a high mAP of 99.3%. However, the YOLOv3 model they employed was large-scale, rendering it difficult to apply in lightweight scenarios.…”
Section: Introductionmentioning
confidence: 99%