AI-Algorithms for Classification & Prediction of Tomato Plant Disease
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Three novel deep convolutional neural networks models are discussed for the classification of tomato plant leaves. The performance of the augmented and original datasets is compared using state-of-the-art models such as AlexNet, GoogLeNet, VGG16, MobileNetV2, SqueezeNet, ResNet-18, ResNet-50, and ResNet-101 with transfer learning. The tomato plant diseases namely early blight, late blight, bacterial spot, leaf mold, mosaic virus, target spot, Septoria leaf spot, yellow leaf curl virus and leaf miner are discussed in this book. Temperature and relative humidity play a major role in susceptible ...
Three novel deep convolutional neural networks models are discussed for the classification of tomato plant leaves. The performance of the augmented and original datasets is compared using state-of-the-art models such as AlexNet, GoogLeNet, VGG16, MobileNetV2, SqueezeNet, ResNet-18, ResNet-50, and ResNet-101 with transfer learning. The tomato plant diseases namely early blight, late blight, bacterial spot, leaf mold, mosaic virus, target spot, Septoria leaf spot, yellow leaf curl virus and leaf miner are discussed in this book. Temperature and relative humidity play a major role in susceptible environmental conditions for plants disease. Forecasting of these parameters is done using models viz. ARIMA, Prophet, Long Short-Term Memory, and Bilinear Long Short-Term Memory with Bayesian optimization.
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