Mechanical Harvesting of Selected Temperate and Tropical Fruit and Nut Trees

Afsah‐Hejri, L.; Homayouni, Taymaz; Toudeshki, Arash; Ehsani, Reza; Ferguson, Louise; García, Sergio Castro

doi:10.1002/9781119851981.ch4

Cited by 18 publications

(12 citation statements)

References 156 publications

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“…The manual labor-intensive process of hand-harvesting fruit is associated with substantial costs [46]. Advancing technologies related to grape-picking robots can enhance grape harvesting efficiency, mitigate labor costs and shortages, and bolster the economic viability of the grape industry [47][48][49].…”

Section: Discussionmentioning

confidence: 99%

An Unstructured Orchard Grape Detection Method Utilizing YOLOv5s

Wang,

Shi,

Liu

et al. 2024

Agriculture

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Rising labor costs and a workforce shortage have impeded the development and economic benefits of the global grape industry. Research and development of intelligent grape harvesting technologies is desperately needed. Therefore, rapid and accurate identification of grapes is crucial for intelligent grape harvesting. However, object detection algorithms encounter multiple challenges in unstructured vineyards, such as similar background colors, light obstruction from greenhouses and leaves, and fruit occlusion. All of these factors contribute to the difficulty of correctly identifying grapes. The GrapeDetectNet (GDN), based on the YOLO (You Only Look Once) v5s, is proposed to improve grape detection accuracy and recall in unstructured vineyards. dual-channel feature extraction attention (DCFE) is a new attention structure introduced in GDN. We also use dynamic snake convolution (DS-Conv) in the backbone network. We collected an independent dataset of 1280 images after a strict selection process to evaluate GDN’s performance. The dataset encompasses examples of Shine Muscat and unripe Kyoho grapes, covering a range of complex outdoor situations. The results of the experiment demonstrate that GDN performed outstandingly on this dataset. Compared to YOLOv5s, this model increased metrics such as 2.02% of mAP0.5:0.95, 2.5% of mAP0.5, 1.4% of precision, 1.6% of recall, and 1.5% of F1 score. Finally, we test the method on a grape-picking robot, and the results show that our algorithm works remarkably well in harvesting experiments. The results indicate that the GDN grape detection model in this study exhibits high detection accuracy. It is proficient in identifying grapes and demonstrates good robustness in unstructured vineyards, providing a valuable empirical reference for the practical application of intelligent grape harvesting technology.

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Section: Discussionmentioning

confidence: 99%

An Unstructured Orchard Grape Detection Method Utilizing YOLOv5s

Wang,

Shi,

Liu

et al. 2024

Agriculture

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“…Innovations in small autonomous machines ( 92 ) may someday allow asynchronous harvesting (and weeding and planting), thereby reducing the soil compaction and fuel use associated with heavy machinery. Machine vision-based “smart” harvesting (e.g., of individual heads or fruits as they ripen) ( 93 ) would allow the design of modern cropping systems that feature greater plasticity of individual plants, genetic variation within crops, and multispecies intercropping: All features of traditional cropping systems with higher habitat complexity than today’s simplified agroecosystems.…”

Section: How Can Domestication Increase Diversity To Enable Agricultu...mentioning

confidence: 99%

The next era of crop domestication starts now

Krug

Drummond

Tassel

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

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Current food systems are challenged by relying on a few input-intensive, staple crops. The prioritization of yield and the loss of diversity during the recent history of domestication has created contemporary crops and cropping systems that are ecologically unsustainable, vulnerable to climate change, nutrient poor, and socially inequitable. For decades, scientists have proposed diversity as a solution to address these challenges to global food security. Here, we outline the possibilities for a new era of crop domestication, focused on broadening the palette of crop diversity, that engages and benefits the three elements of domestication: crops, ecosystems, and humans. We explore how the suite of tools and technologies at hand can be applied to renew diversity in existing crops, improve underutilized crops, and domesticate new crops to bolster genetic, agroecosystem, and food system diversity. Implementing the new era of domestication requires that researchers, funders, and policymakers boldly invest in basic and translational research. Humans need more diverse food systems in the Anthropocene—the process of domestication can help build them.

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“…Postharvest handling has a significant impact on the quality and storage of agricultural produce [4]. Improper postharvest handling can diminish the quality of fruit products and lower their selling price [5]. This includes the transportation of fresh products from farmers to markets or consumers during the postharvest period [6].…”

Section: Introductionmentioning

confidence: 99%

Color and Firmness Quality Changes of Java Apple During Postharvest Transportation and Storage

Iswahyudi,

Darmawati,

Mardjan

et al. 2024

Curr. Appl. Sci. Technol.

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Transportation vibration adversely affects fresh produce during transportation. In addition, storage temperature affects the quality of fresh commodities. The physical changes in Java apples during transportation and storage were evaluated in this study. Java apples were transported from local farms to wholesale markets (180 km). Java apples were stored at room temperature (28°C) for six days. Physical qualities such as weight loss and firmness of the Java apple samples were evaluated. The RGB image acquisition system was used to assess changes in the color of the Java apple. The vibration showed that more than 70% of the acceleration occurred between 220-290 cm/s2 in the vertical and horizontal directions during transportation. Analysis showed that physical qualities, such as weight loss and firmness, were strongly affected by the packaging used, vibration during transportation, and storage temperature. The weight loss and reduction in firmness was highest in Java apples transported using wholesaler packaging (packaging A). The lightness, yellowness, and hue values decreased significantly because transportation vibration was relatively high, and the Java apples were stored at room temperature. Redness, total color difference, and color index increased significantly in Java apples that were transported using package A and stored at room temperature. The results showed that the use of transportation packaging affected changes in the physical quality of Java apples. Packaging A generally increase in weight loss, hardness, and changes in fruit color than other packaging types.

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Mechanical Harvesting of Selected Temperate and Tropical Fruit and Nut Trees

Cited by 18 publications

References 156 publications

An Unstructured Orchard Grape Detection Method Utilizing YOLOv5s

An Unstructured Orchard Grape Detection Method Utilizing YOLOv5s

The next era of crop domestication starts now

Color and Firmness Quality Changes of Java Apple During Postharvest Transportation and Storage

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