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<p>Robotic harvesting systems face significant challenges in detecting sweet peppers due to the visual similarities between the fruit and its leaves in color and shape. Unlike fruits such as apples or tomatoes, where differences in shape and color facilitate straightforward identification through visible imaging, sweet pepper detection requires more advanced techniques. This study introduces a distinctive method that leverages the temperature differential between sweet peppers and their leaves, primarily due to the hollow structure of the fruit. The hollow interior of sweet peppers, which contains air, causes the fruit’s temperature to change more slowly compared to the leaves, whose temperature fluctuates rapidly due to transpiration. This difference in thermal behavior under varying greenhouse conditions, including temperature, humidity, and illuminance, is exploited for detection. Two experimental phases were conducted to assess the effectiveness of this detection approach. The first experiment utilized a far-infrared camera, alongside temperature, humidity, and illuminance sensors, to capture and analyze thermal variations between the sweet peppers and their surrounding leaves. The results revealed a significant temperature differential, serving as a reliable indicator for fruit detection. In the second experiment, a depth camera was integrated to enhance the detection process, enabling the identification of sweet peppers within the robotic arm’s reachable range. This integration aimed to enhance the system’s ability to distinguish sweet peppers from leaves in three-dimensional space, improving detection precision for robotic harvesting. The experimental results demonstrate that the proposed thermal-based detection method shows significant promise for accurately identifying sweet peppers in greenhouse environments. This approach offers a valuable solution for robotic harvesting systems, particularly for crops where conventional visible imaging techniques are insufficient. However, the method’s applicability may be limited to hollow fruits like sweet peppers, as fruits with compact structures may not exhibit similar thermal characteristics.</p>

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Sweet pepper detection in day and night greenhouse environments using thermal and depth imaging

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月6日

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  • Zinat Tasneem,
  • Koichi Oka,
  • Naoya Tada

摘要

Robotic harvesting systems face significant challenges in detecting sweet peppers due to the visual similarities between the fruit and its leaves in color and shape. Unlike fruits such as apples or tomatoes, where differences in shape and color facilitate straightforward identification through visible imaging, sweet pepper detection requires more advanced techniques. This study introduces a distinctive method that leverages the temperature differential between sweet peppers and their leaves, primarily due to the hollow structure of the fruit. The hollow interior of sweet peppers, which contains air, causes the fruit’s temperature to change more slowly compared to the leaves, whose temperature fluctuates rapidly due to transpiration. This difference in thermal behavior under varying greenhouse conditions, including temperature, humidity, and illuminance, is exploited for detection. Two experimental phases were conducted to assess the effectiveness of this detection approach. The first experiment utilized a far-infrared camera, alongside temperature, humidity, and illuminance sensors, to capture and analyze thermal variations between the sweet peppers and their surrounding leaves. The results revealed a significant temperature differential, serving as a reliable indicator for fruit detection. In the second experiment, a depth camera was integrated to enhance the detection process, enabling the identification of sweet peppers within the robotic arm’s reachable range. This integration aimed to enhance the system’s ability to distinguish sweet peppers from leaves in three-dimensional space, improving detection precision for robotic harvesting. The experimental results demonstrate that the proposed thermal-based detection method shows significant promise for accurately identifying sweet peppers in greenhouse environments. This approach offers a valuable solution for robotic harvesting systems, particularly for crops where conventional visible imaging techniques are insufficient. However, the method’s applicability may be limited to hollow fruits like sweet peppers, as fruits with compact structures may not exhibit similar thermal characteristics.