Detecting Plant Diseases with Artificial Intelligence

Paper: https://www.researchgate.net/publication/361511874_Color-Aware_Two-Branch_DCNN_for_Efficient_Plant_Disease_Classification

Deep convolutional neural networks (DCNNs) have been successfully applied to plant disease detection. Unlike most existing studies, we propose feeding a DCNN CIE Lab instead of RGB color coordinates. We modified an Inception V3 architecture to include one branch specific for achromatic data (L channel) and another branch specific for chromatic data (AB channels). This modification takes advantage of the decoupling of chromatic and achromatic information. Besides, splitting branches reduces the number of trainable parameters and computation load by up to 50% of the original figures using modified layers. We achieved a state-of-the-art classification accuracy of 99.48% on the Plant Village dataset and 76.91% on the Cropped-PlantDoc dataset.

Source code: https://github.com/joaopauloschuler/two-branch-plant-disease

Paper: https://www.researchgate.net/publication/361511874_Color-Aware_Two-Branch_DCNN_for_Efficient_Plant_Disease_Classification

#DeepLearning #ConvolutionalNeuralNetworks #PlantDiseaseDetection #DCNN #CIELab #InceptionV3 #AchromaticData #ChromaticData #MachineLearning #AIInAgriculture #PlantVillageDataset #PlantDocDataset #ClassificationAccuracy #AIResearch #ComputerVision #ModifiedInceptionV3 #EfficientAI #NeuralNetworkOptimization #SmartFarming #AgriculturalAI #TechInnovation #DataScience #PrecisionAgriculture