ABSTRACT

The changes in weather have beneficial and harmful effects on crop yields. There will be a loss of yield because of the diseases in crops. With the growing population, the fundamental want of food is growing. That is why agriculture gains a prominent position all around the world. It eventually ends up by a massive defeat for the farmers and the financial boom of India. The article’s primary goalis to bring together farmers and cutting-edge technologies to minimise diseases in plant leaves. To enforce the idea, ‘Tomato’ is selected in which leaf sicknesses are expected and identified by the Artificial Intelligence algorithms, CNN (Convolution Neural Network) with pc technological know-how. Tomato is a mere consumable vegetable in India. In this investigation, seven types of tomato leaf disorders were sensed, including one wholesome elegance. The farmers are able to check the symptoms with the shapes of images of the tomato leaves with those expecting diseases. Its comparison of various classification and filters/methods with different techniques, such as K-Means classifier, SVM (Support Vector), RBF(Radial Basis Function) Kernel, Optimised MLP(Multilayer perceptron), NN classifier, BPNN (back-propagation neural network) and CNN Classifier. The classification accuracy of the existing method after experiment is RBF − 89%, k-means – 85.3%, SVM – 88.8%, Optimised MLP – 91.4%, NN – 97, BPNN – 85.5%, CNN – 94.4%. The proposed architecture can achieve the desired accuracy of 99.4%.

摘要

天气的变化对作物产量有有利和有害的影响。由于作物病害，将导致产量损失。随着人口的增长，食物的基本需求也在增加。这就是为什么农业在世界范围内获得突出地位的原因。它最终以农民的巨大失败和印度的金融繁荣而告终。这篇文章的主要目标是汇集农民和尖端技术，以尽量减少植物叶子中的疾病。为了实施这个想法，选择了“番茄”，其中预计会出现叶病，并通过人工智能算法、CNN（卷积神经网络）和电脑技术诀窍进行识别。番茄在印度只是一种可食用的蔬菜。在这项调查中，检测到了七种番茄叶片疾病，包括一种有益健康的优雅。农民可以通过番茄叶子的图像形状来检查症状和那些期待疾病的人。它比较了各种分类和过滤器/方法与不同技术，例如 K-Means 分类器、SVM（支持向量）、RBF（径向基函数）内核、优化的 MLP（多层感知器）、NN 分类器、BPNN（反向传播神经网络）网络）和 CNN 分类器。现有方法在实验后的分类准确率是RBF-89%、k-means-85.3%、SVM-88.8%、Optimized MLP-91.4%、NN-97、BPNN-85.5%、CNN-94.4% RBF（径向基函数）内核、优化的 MLP（多层感知器）、NN 分类器、BPNN（反向传播神经网络）和 CNN 分类器。现有方法在实验后的分类准确率是RBF-89%、k-means-85.3%、SVM-88.8%、Optimized MLP-91.4%、NN-97、BPNN-85.5%、CNN-94.4% RBF（径向基函数）内核、优化的 MLP（多层感知器）、NN 分类器、BPNN（反向传播神经网络）和 CNN 分类器。现有方法在实验后的分类准确率是RBF-89%、k-means-85.3%、SVM-88.8%、Optimized MLP-91.4%、NN-97、BPNN-85.5%、CNN-94.4%. 所提出的架构可以达到 99.4% 的预期准确率。