LEAF DISEASE DETECTION OF CUCURBITS USING CNN

Ishaan Agrawal, Prada Hegde, P. Shetty, Priyanka Shingane
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引用次数: 2

Abstract

Identification of plant disease is tough in agribusiness arena. If it is inaccurate, there occurs a tremendous damage in the production and economical price. Leaf Disease detection requires huge amount of work, knowledge, processing time in plant disease. The most used and edible vegetable all over the world is from cucurbitaceous family. The crops under this family have great economic value in the food industry and its production is done in large scale. This family consists of 965 species. If any of these plants catch disease then there would be a tremendous loss in the production of this field yields. Thus, treating them at early stage is best way to prevent such losses. Hence, Deep Learning Algorithm like CNN can be used to detect the diseases of the plants. The leaves of the plants would be used as primary material for identification of the disease, as they are much more visible on the leaves.
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利用CNN检测葫芦叶病
在农业领域,植物病害的鉴定是困难的。如果不准确,将对生产和经济价格造成巨大损失。在植物病害中,叶片病害检测需要大量的工作、知识和处理时间。葫芦科蔬菜是世界上使用最多、食用最多的蔬菜。该科作物在食品工业中具有较大的经济价值,生产规模较大。这个科有965种。如果这些植物中的任何一种染上了疾病,那么这片田地的产量就会受到巨大的损失。因此,早期治疗是防止此类损失的最好方法。因此,像CNN这样的深度学习算法可以用来检测植物的病害。植物的叶子将被用作鉴定疾病的主要材料,因为它们在叶子上更明显。
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