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• W4387048897 abstract "Plant disease detection is an important aspect of modern agriculture that is crucial for ensuring crop productivity and quality. Sweet lime is an important citrus fruit, and its leaves are susceptible to a range of diseases that can significantly impact its yield. However, the lack of publicly available data on sweet lime diseases has made the development of effective detection systems challenging. To address this problem, the authors of this paper developed their own dataset of sweet lime leaves containing 4000 images. The dataset was carefully curated to ensure diversity and accuracy, and it was used to train a fine-tuned customised single-shot detector (SSD) model. The SSD is a popular object detection algorithm that is known for its speed and accuracy, making it well-suited for this task. The results of the evaluation showed that the proposed approach achieved impressive performance metrics. The model had an accuracy of 99%, which indicates that it was able to correctly identify the presence or absence of diseases in the sweet lime leaves with high confidence. The mean intersection over union (mIoU) of 97% is a measure of how well the model was able to detect the boundaries of the diseased areas, indicating that it was able to accurately localize the diseases. The model’s inference time of 16ms and frames per second (FPS) of 60 demonstrate that it is fast enough to be used in real-time applications. Finally, the mean average precision (mAP) of 0.97 is a measure of how well the model was able to rank the detected diseases by their severity, demonstrating its effectiveness in prioritizing diseases for further action. These findings have important practical implications for agricultural management. The proposed approach could be used to monitor sweet lime orchards for diseases and to identify the specific types of diseases present. This information could be used to make targeted interventions, such as applying fungicides or removing infected leaves, to prevent the spread of diseases and ensure the health and productivity of the crop. Furthermore, the methodology used in this study could be adapted for use with other crops and diseases, expanding its potential impact in the field of agriculture." @default.
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• W4387048897 date "2023-01-01" @default.
• W4387048897 modified "2023-09-27" @default.
• W4387048897 title "Fine Tuned Single Shot Detector for Finding Disease Patches in Leaves" @default.
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• W4387048897 doi "https://doi.org/10.1007/978-3-031-43605-5_1" @default.
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