KI-Algorithmen zur Klassifizierung und Vorhersage von Krankheiten bei Tomatenpflanzen

Three novel deep neural convolutional network models are discussed for the classification of tomato leaves. The performance of the extended and original datasets is compared with state-of-the-art models such as AlexNet, GoogLeNet, VGG16, MobileNetV2, SqueezeNet, ResNet-18, ResNet-50, and ResNet-101 using transfer learning. The plant diseases affecting tomatoes, namely blight, bacterial spots, leaf mold, mosaic virus, target spots, Septoria leaf spots, yellow leaf curl virus, and leaf miner, are addressed in this book. Temperature and relative humidity play a crucial role in the susceptibility of environmental conditions to plant diseases. The prediction of these parameters is carried out using models such as ARIMA, Prophet, Long Short-Term Memory, and bilinear Long Short-Term Memory with Bayesian optimization.