Abstract
As they have nutritional, therapeutic, so values, plants were regarded as important and they’re the main source of humankind’s energy supply. Plant pathogens will affect its leaves at a certain time during crop cultivation, leading to substantial harm to crop productivity & economic selling price. In the agriculture industry, the identification of fungal diseases plays a vital role. However, it requires immense labor, greater planning time, and extensive knowledge of plant pathogens. Computerized approaches are developed and tested by different researchers to classify plant disease identification, and that in many cases they have also had important results several times. Therefore, the proposed study presents a new framework for the recognition of fruits and vegetable diseases. This work comprises of the two phases wherein the phase-I improved localization model is presented that comprises of the two different types of the deep learning models such as You Only Look Once (YOLO)v2 and Open Exchange Neural (ONNX) model. The localization model is constructed by the combination of the deep features that are extracted from the ONNX model and features learning has been done through the convolutional-05 layer and transferred as input to the YOLOv2 model. The localized images passed as input to classify the different types of plant diseases. The classification model is constructed by ensembling the deep features learning, where features are extracted dimension of from pre-trained Efficientnetb0 model and supplied to next 07 layers of the convolutional neural network such as 01 features input, 01 ReLU, 01 Batch-normalization, 02 fully-connected. The proposed model classifies the plant input images into associated labels with approximately 95% prediction scores that are far better as compared to current published work in this domain.
Highlights
The emergence of plant pathogens has a detrimental impact on crop development, if plant pathogens are not identified timely, there would be a rise in food poverty
The transfer learning models, that mitigates the issues caused by traditional neural networks, i.e., these same remedies composed of utilizing a pre-trained model where parameters of last layers need to be extrapolated from the scratch that is normally utilized in the real time application [21]
The localization model is built by a combination of two convolutional neural models, where deep features learning is performed using an open neural network such as ONNX and extracts features from the convolutional-05 layers and transferred as input to the tinyYOLOv2
Summary
The emergence of plant pathogens has a detrimental impact on crop development, if plant pathogens are not identified timely, there would be a rise in food poverty. The early indicator and prediction seem to be the source of efficient prevention and treatment for crop ailments [2] They play key responsibility for management and decision support systems for agricultural development [3]. The observations made by seasoned farmers are the predominant method for plant ailments identification in rural regions of advanced nations; this involves constant supervision of specialists, and that could be extremely costly in agricultural activities. There seem to be a variety of features required to be learned for Convolutional Neural Network (CNN) and its derivatives, training certain Neural networks often needs several labelled data and significant computing resources by scratch to determine the efficiency. After localization, comprehensive features analysis is performed through a pre-trained Efficientnetb0 model and 07 layers CNN model with softmax layer for classification of different types of plant diseases.
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