Plant Disease Detection Using Machine Learning & Image Processing

Malathi T, Muhammed Nayif M Navab Metha, Nourin S, Prince Sajuvin, Jaison Mathew John
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Abstract

Most of individuals on earth make their living for the most part from horticultural work. Assuming there are any issues in that essential area, the populace's way of life will endure. Accordingly, it's vital for keep the agribusiness area in the right equilibrium by protecting something very similar from destructive impacts like plant sicknesses, dryness, and so on. In the rural area, ranchers get more cash-flow from cultivation than from different yields. These plants are helpless against numerous sicknesses rapidly, and early manual illness determination in crops is extremely difficult. stage. AI methods are utilized instead of manual illness distinguishing proof, which could prompt blunders. Picture.The impacted region of the picture is caught to finish the handling. Keyword: Image Processing, Resnet, Convolution Neural Network (CNN), Random Forest, Plant Diseases.
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利用机器学习和图像处理检测植物病害
地球上的大多数人都以园艺工作为生。如果这一重要领域出现任何问题,民众的生活方式将难以为继。因此,通过保护类似的东西免受植物疾病、干旱等破坏性影响,使农业综合区保持适当的平衡至关重要。在农村地区,牧场主从种植中获得的现金流比从不同产量中获得的现金流要多。这些植物无法迅速抵御多种病害,而早期人工判断农作物的病害是非常困难的。利用人工智能方法代替人工辨别疾病证明,可能会导致失误。图片:捕捉图片中受影响的区域,完成处理。关键词: 图像处理、Resnet、卷积神经网络(CNN)、随机森林、植物病害。
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