Jameer Kotwal

This manuscript delineates the code developed for a published scholarly article aimed at supporting researchers in addressing plant leaf disease detection and classification (PLDC) challenges while evaluating the efficacy of various deep learning models. Furthermore, the research incorporates preprocessing strategies, correlation, segmentation employing the UNet model, feature extraction methods and EfficientNet model. The software model generates graphs such as confusion matrix, ROC curve (Receiver Operating Characteristic), and visual representations of loss and accuracy graphs. The initial research was disseminated in the Multimedia Tools and Applications journal, and the accompanying dataset was also introduced in the Data in Brief journal. • Optimized EfficientNet deep learning model. • The U-Net model is used for image segmentation where the weights are updated to extract the specific region from leaf to classify the disease. • Feature extraction technique is used to extract the features like texture, shape, color etc. from the segmented image. • Our software model learns the features to solve over-fitting issues and vanishing gradient issues. • Unwanted errors are also addressed by our model for classifying the diseases.

Intelligent agriculture heavily relies on the science of agricultural disease image recognition. India is also responsible for large production of French beans, accounting for 37.25% of total production. In India from south region of Maharashtra state this crop is cultivated thrice in year. Soyabean plant is planted between the months of June through July, during the months of October and September during the rabi season, as well as in February. In the Maharashtrian regions of Pune, Satara, Ahmednagar, Solapur, and Nashik, among others, Soyabean plant is a common crop. In Maharashtra, Soyabean plant is grown over an area of around 31,050 hectares. This research presents a dataset of leaves from soyabean plants that are both insect-damaged and healthy. Images were taken over the course of fewer than two to three seasons on several farms. There are 3363 photos altogether in the seven folders that make up the dataset. Six categories comprise the dataset: I) Healthy plants II) Vein Necrosis III) Dry leaf IV) Septoria brown spot V) Root images VI) Bacterial leaf blight. This study's goal is to give academics and students accessibility to our dataset so they may use it for their studies and to build machine learning models.

In the systems of Industry 4.0, data analytics increasingly plays a bigger role. Big data has the potential to create unique, groundbreaking opportunities for the power grid industry, enhancing a global range of societal and economic benefits. A layer of information has been added to the traditional approach for transmission of electricity and scattered network, and as a result of that, it increases the installation of smart meters and sensors, which is being driven by the advancement of information and communication technology. The gathering, storing, and processing of data will be done using this layer. In the energy sector, the big data utilization or use is more. Particularly when it comes to the integration of renewable energy sources with smart networks, there are significant and encouraging obstacles. This chapter discusses the benefits and challenges of using big data analytics for renewable energy power facilities. A crucial component is the capacity to gather facts and apply them effectively for better judgment. A framework was developed for potential big data analytics applications in smart grids and renewable energy power plants. Five different machine learning approaches are used in five steps to anticipate the smart grid’s stability. The dataset’s extremely small amount of data is the work’s primary shortcoming, but the real-time event analysis and cloud computing that were provided were acceptable for the big data analytics framework. Future studies should use larger datasets that reflect global demand for a variety of renewable energy sources.