Plant Disease Detection and Recognition using K means Clustering

K. Sankaran, N. Vasudevan, V. Nagarajan
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引用次数: 7

Abstract

Crop cultivation plays a vital role in Agriculture. Above 70% of the people are in the agriculture field. Currently, the inefficient of the food materials are appropriate by the crop infection, where the production rate is reduced. It is because of the pesticides used in the agriculture field that leads to the diseases among the plants. This work explores the leaf detection and finds whether it is affected by the diseases or not. That makes the initial step to controlling the disease from spreading. A proposed method of enhanced k means clustering (EKMC) for crop early prediction the plant on image segmentation and masking of the green pixel. And finally, it detects the normal and abnormal leaf.
基于K均值聚类的植物病害检测与识别
作物种植在农业中占有重要地位。70%以上的人从事农业。目前,由于作物侵染,粮食原料的效率低下,导致产量下降。正是由于农田中使用的农药导致了植物间的病害。本工作对叶片检测进行了探索,发现其是否受到病害的影响。这是控制疾病传播的第一步。提出了一种基于图像分割和绿色像素遮挡的增强k均值聚类(EKMC)作物早期预测方法。最后,它检测正常和不正常的叶子。
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