人工神经网络在利用叶片图像进行水稻病害分类中的应用

Nandi Sunandar, Joko Sutopo
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摘要

水稻是世界上人类需要的一种植物的名称。世界上大多数人,尤其是亚洲人,都把水稻作为主要的能源。水稻植物的重要性使得水稻在各个地区广泛种植。大多数人将水稻作为主食作物。因此,需要考虑水稻生产,以满足世界上大多数人对足够食物的需求。最大限度地提高水稻产量需要考虑的主要问题是,在看护水稻植株时,许多抑制水稻植株的因素都可能成为各地区粮食危机的原因。因此,需要考虑对水稻生产的看护。水稻减产除了水土养分不足外,还需要考虑植物病害。经常侵袭水稻植株的病害包括细菌性叶枯病、褐斑病和左旋灰霉病。因此,在病害更广泛地侵袭水稻植株之前,需要了解预防工作或早期治疗的知识。在解决这一问题时,我们可以利用人工智能技术。人工智能是利用水稻植株叶片上的图像来检测水稻植株的病害类型。如果能检测出水稻植株的病害,就能使水稻种植者更容易克服病害。从使用该算法识别水稻病害类型的研究结果来看,ANN(人工神经网络)算法可用于解决这一问题,其准确率达到 83%。这表明人工智能在病害识别方面的能力可以帮助农民发现病害类型。
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Utilization of Artificial Neural Network in Rice Plant Disease Classification Using Leaf Image
Rice is the name of the type of plant that is needed by humans in the world. The plant is used as the main source of energy by Most people in the world, especially on the Asian continent. The importance of rice plants makes rice widely planted in various regions. Most humans use rice as a staple crop. Therefore, rice production needs to be considered to meet the need for enough food for most people in the world. The main thing that needs to be considered in maximizing rice production is that when guarding rice plants, many factors that inhibit rice plants can be the cause of food crises in various regions. Therefore, the care of rice production needs to be considered. In addition to the lack of nutrients in water and soil in decreasing rice production, plant diseases also need to be considered. Some types of diseases that often attack rice plants include bacterial leaf blight, brown spots, and left smut. Therefore, there is knowledge of prevention efforts or early treatment before the disease attacks rice plants more widely. The efficacy of technology can be used in solving this problem, we can take advantage of artificial intelligence in it. Artificial intelligence is implemented for the detection of types of diseases in rice plants using image images on the leaves of rice plants. If the disease in rice plants can be detected, it will make it easier for rice plant farmers to overcome the disease. The ANN (Artificial neural network) algorithm can be used in this problem from the results of research on identifying the type of rice disease using the algorithm obtained an accuracy of 83%. This shows the ability of artificial intelligence in disease identification can help farmers detect types of diseases.
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