Background: Agriculture is a major driver of economic expansion and accounts for 17% of India&rsquo;s GDP. However, the loss of agricultural land is a major problem that affects the economy and farmers&rsquo; livelihoods. Farmers use non-scientific techniques to detect pomegranate diseases, which takes time. A detection and classification system based on DL provides a quicker and more precise answer. 
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Methods: This paper presents a deep learning-based framework for the categorization and detection of pomegranate fruit diseases. For disease identification, five Deep CNN models are used: VGG16, MobileNetV2, ResNet50V2, DenseNet121 and the suggested PomeNetV2. 5,099 photos from five classes in a custom pomegranate fruit dataset are used. Pre-processing methods like data augmentation, resizing and rescaling are used to improve model performance and reduce overfitting. After that, the dataset is divided into 20% for testing and 80% for training. 
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Result: The confusion matrix, precision, recall, accuracy and F1-score were used to evaluate the performance of the suggested models. DenseNet201 did marginally better with 98% accuracy, according to the data, while VGG16, MobileNetV2 and ResNet50V2 all attained 97% accuracy. The PomeNetV2 model achieved the greatest accuracy of 99.02%, outperforming all other models.

Performance Evaluation of Deep Learning Models for Multiclass Disease Detection in Pomegranate Fruits

<div style="text-align:justify">Background: India is mainly an agriculturist country where agriculture is a very important sector for Indian economy. Different varieties of crops are grown in huge acres of land yielding sufficient quantity. But due to frequent attacks of pests and pathogens the plants gets infected and develops different diseases. These diseases affect the quality of crop production and put the consumer&rsquo;s health at risk. Thus, it is important to keep a track on the health of the plants on a regular basis so that required action can be taken without wasting any time. Therefore the study aimed to propose a method for automatic classification of healthy and diseased plant leaves using transfer learning approach.&nbsp; 
Methods: Deep learning is gaining popularity among researchers due to its several advantages. Two approaches were used in deep learning: one is to build a model from scratch and the other to use pre-trained models (PTMs). This concept is known as transfer learning. It is based on the concept where the network learns from existing models and uses the knowledge to solve a completely different problem. These models are trained on huge datasets to solve a task that is similar to our desired problem. Mobile Net V2 has been used for our research problem and the beans dataset which is a publicly available dataset.&nbsp; 
Result: Mobile Net V2 is a very light weight model which is trained on more than 1.4 M images. We have downloaded the pre-trained model and fine tuned the last layer to solve our desired task. The results observed that in just only 30 epochs our proposed model could achieve a classification accuracy of 98%. Also, we have compared our proposed method with other existing methods in literature and found that our proposed method had higher classification accuracy than other existing methods.&nbsp;&nbsp;</div>

Performance Evaluation of Deep Learning Models for Multiclass Disease Detection in Pomegranate Fruits

A Transfer Learning Approach for Leaf Image based Classification of Healthy and Diseased Leaves