Early stage detection of Downey and Powdery Mildew grape disease using atmospheric parameters through sensor nodes

Grape diseases are major factors causing severe diminution in its fruit development.Unfavorable climatic conditions are one of the principal dangers for grape disease development.DownyMildew,PowderyMildew,Anthracnose,Stem borer,Black Rot,Leaf Blight are widespread grape leaf vermin and diseases,which cause stern monetary losses to the grape industry.Devices ready to quantify the climate conditions in real-time for disease onset are hence crucial to performtimely diagnosis and precise detection of grape leaf diseases.This will ensure the healthy growth of grape plants,further controlling the spread of diseases.This paper discusses the requirements for building a consistent grape disease detection framework that would encourage headways in agribusiness.The primary aim of this work is to adapt an Internet of Things(IoT)based approach to predict the occurrence of Downey and Powdery Mildew grape diseases at an early stage.The sensor values received are transmitted to the Central Server with the help of the IoT device NodeMCU.At the server side,an analysis is made based on weather conditions.Further notification to the farmer is sent ifweather properties are conducive for disease onset.The exclusivity of the systemlies in using a rain gauge sensor along with the temperature sensor to predict the occurrence of grape diseases.This systemrealizes an overall accuracy of 94.4%for Downey Mildew and 96%for PowderyMildew.Experimental results suggest the projected model can proficiently recognize Downey and Powdery Mildew grape diseases.

Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks

Grape diseases are main factors causing serious grapes reduction.So it is urgent to develop an automatic identification method for grape leaf diseases.Deep learning techniques have recently achieved impressive successes in various computer vision problems,which inspires us to apply them to grape diseases identification task.In this paper,a united convolutional neural networks(CNNs)architecture based on an integrated method is proposed.The proposed CNNs architecture,i.e.,UnitedModel is designed to distinguish leaves with common grape diseases i.e.,black rot,esca and isariopsis leaf spot from healthy leaves.The combination of multiple CNNs enables the proposed UnitedModel to extract complementary discriminative features.Thus the representative ability of United-Model has been enhanced.The UnitedModel has been evaluated on the hold-out PlantVillage dataset and has been compared with several state-of-the-art CNN models.The experimental results have shown that UnitedModel achieves the best performance on various evaluation metrics.The UnitedModel achieves an average validation accuracy of 99.17%and a test accuracy of 98.57%,which can serve as a decision support tool to help farmers identify grape diseases.

Recognition of grape leaf diseases using MobileNetV3 and deep transfer learning

Timely diagnosis and accurate identification of grape leaf diseases are decisive for controlling the spread of disease and ensuring the healthy development of the grape industry.The objective of this research was to propose a simple and efficient approach to improve grape leaf disease identification accuracy with limited computing resources and scale of training image dataset based on deep transfer learning and an improved MobileNetV3 model(GLD-DTL).A pre-training model was obtained by training MobileNetV3 using the ImageNet dataset to extract common features of the grape leaves.And the last convolution layer of the pre-training model was modified by adding a batch normalization function.A dropout layer followed by a fully connected layer was used to improve the generalization ability of the pre-training model and realize a weight matrix to quantify the scores of six diseases,according to which the Softmax method was added as the top layer of the modified networks to give probability distribution of six diseases.Finally,the grape leaf diseases dataset,which was constructed by processing the image with data augmentation and image annotation technologies,was input into the modified networks to retrain the networks to obtain the grape leaf diseases recognition(GLDR)model.Results showed that the proposed GLD-DTL approach had better performance than some recent approaches.The identification accuracy was as high as 99.84%while the model size was as small as 30 MB.