利用YOLO进行作物病害检测

Achyut Morbekar, Ashi Parihar, R. Jadhav
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引用次数: 18

摘要

农业是印度数百万农民的累积活动。种植者在选择合适的作物方面有广泛的多样性。但是由于知识的缺乏,农民对影响农场的各种疾病都很茫然。许多农民在收割有病的作物上挣扎并浪费了大量时间。及时评估问题对于避免重大损失和提高生产是必要的。该系统利用一种新的目标检测技术YOLO(You Only Look Once)来检测植物病害。YOLO实时处理叶子图像的速度为45帧/秒,比其他目标检测技术要快。在对图像进行处理之前,将图像划分为若干网格单元。边界框和类别概率由单个神经网络在一次评估中预测。这有效地提高了叶片疾病检测的速度和准确性。
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Crop Disease Detection Using YOLO
Agriculture is the cumulative activity for millions of farmers in India. Planters have a wide range of diversity for selecting suitable crops. But due to scarcity of knowledge, farmers are in a daze about kinds of diseases that affect the farm. Many farmers struggle and waste much of their time in reaping diseased crops. The timely assessment of the problem is necessary to avert major damage and enhance production. The proposed system makes use of a novel approach of the object detection technique to detect plant disease, YOLO(You Only Look Once). YOLO processes leaf images at 45 frames per second in real-time, which is faster than other object detection techniques. It divides the image into several grid cells before processing the image. The bounding boxes and class probabilities are predicted by a single neural network in just one evaluation. This effectively boosts the speed and accuracy of disease detection on the leaf.
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