Automatic identification methods for the early detection of disease in plants play a significant role in precision crop protection. Various methods have been employed in the task of plant disease recognition. This work benefits in actual identification of a plant and further detection of disease in them. In this paper, the leaf images of 9 different plants with 32 different classes of the PlantVillage database are analyzed for the process. The main contribution of this work is to classify the plant leaf disease with the proposed network-based on AlexNet and comparing with the traditional support vector machine. The convolutional neural network is used to detect the plant leaf and identify the healthy and diseased plant through this network. The mixed combination of healthy and diseased plant leaf data is used for training the convolutional neural network. Transfer learning is used for the pre-trained AlexNet network for a different amount of data for training of the network, and results are validated with a support vector machine and deep learning classifier. AlexNet performed well with an accuracy of 91.15% as compared to SVM giving 88.96% and 89.69% for radial basis function kernel and linear kernel respectively.

Precision crop safety relies on automated systems for detecting and classifying plants. This work proposes the detection and classification of nine species of plants of the PlantVillage dataset using the proposed developed compact convolutional neural networks and AlexNet with transfer learning. The models are trained using plant leaf data with different data augmentations. The data augmentation shows a significant improvement in classification accuracy. The proposed models are also used for the classification of 32 classes of the Flavia dataset. The proposed developed N1 model has a classification accuracy of 99.45%, N2 model has a classification accuracy of 99.65%, N3 model has a classification accuracy of 99.55%, and AlexNet has a classification accuracy of 99.73% for the PlantVillage dataset. In comparison to AlexNet, the proposed models are compact and need less training time. The proposed N1 model takes 34.58%, the proposed N2 model takes 18.25%, and the N3 model takes 20.23% less training time than AlexNet. The N1 model and N3 models are size 14.8 MB making it 92.67% compact, and the N2 model is 29.7 MB which makes it 85.29% compact as compared to AlexNet. The proposed models are giving good accuracy in classifying plant leaf, as well as diseases in tomato plant leaves.

Agriculture in Maharashtra has immense importance in India, acting as the back-bone of the economy and a primary livelihood source for a significant population. Being the third largest state in India, Maharashtra has a high scale crop production in the country which also has an important impact on the economy. Initially the study focus on developing predictive models that guide farmers in selecting suitable crops for the divisions in the state of Maharashtra. This study presents a Crop Recommendation System (CRS) designed to support Maharashtra’s agricultural sector by utilizing a comprehensive dataset from 2001 to 2022 provided by the India Meteorological Department. This study helps in improvising technical efficiency and productivity of the farmers. Harvesting crops in optimal condition can help to produce efficient harvest hence the research concentrates on providing best crop recommendation system (CRS) with the help of Machine Learning and Deep Learning techniques. The data, enhanced for accuracy using expectation-maximization optimization, underpins predictive models that guide crop selection. EM contributes to a more robust and reliable dataset for subsequent analyses and modeling by iterative estimating and updating missing values based on probabilistic expectations. Key findings show that the Random Forest algorithm excels in predicting suitable crops with 92% accuracy. Further precision is achieved through a Long Short-Term Memory network forecasting weather patterns three months ahead, accommodating temporal data variations. Subsequently, the proposed system leverages these forecasts to recommend five ideal crops per division within Maharashtra, aiding farmers’ decision-making and adapting to regional climatic conditions. A supplementary crop calendar offers monthly district-specific planting guidance. An intuitive Graphical User Interface delivers this information effectively, ensuring practical and informed agricultural choices across the state. In essence, the study provides an innovative tool for enhancing economic stability and sustenance in Maharashtra through technology-driven agriculture recommendations aligned with future weather expectations.

Parkinson's disease (PD) is a neurodegenerative disorder that predominantly affects the motor system. Diffusion magnetic resonance imaging (MRI) has demonstrated deficits in anisotropy as well as increased diffusivity in the sub-cortical structures, primarily in the substantia nigra in PD. However, the clinical spectrum of PD is not limited to motor symptoms; rather, it encompasses several nonmotor symptoms such as depression, psychosis, olfactory dysfunction, and cognitive impairment. These nonmotor symptoms underscore PD as a complex neurological disorder arising from dysfunction of several network components. Therefore, to decipher the underlying neuropathology, it is crucial to employ novel network-based methods that can elucidate associations between specific network changes. This study aimed at assessing the large-scale structural network changes in PD. Structural connectomes were computed by using probabilistic fiber tracking on diffusion MRI between 86 regions of interest. Graph theoretic analysis on the connectome was carried out at several levels of granularity: global, local (nodal), lobar, and edge wise. Our findings demonstrate lower network clustering capability, overall lower neural connectivity, and significantly reduced nodal influence of the hippocampus in PD. In addition, extensive patterns of reduced connectivity were observed within and between the temporal, parietal, and occipital areas. In summary, our findings corroborate widespread structural disconnectivity that can be potentially linked to the nonmotor symptoms in PD.

The banking sector's shift from traditional physical locations to digital channels has offered customers unprecedented convenience and increased the risk of fraud for customers and institutions alike. In this study, we discuss the pressing need for robust fraud detection & prevention systems in the context of evolving technological environments. We introduce a graph-based machine learning model that is specifically designed to detect fraudulent activity in various types of banking operations, such as credit card transactions, debit card transactions, and online banking transactions. This model uses advanced methods for anomalies, behaviors, and patterns to analyze past transactions and user behavior almost immediately. We provide an in-depth methodology for evaluating fraud detection systems based on parameters such as Accuracy Recall rate and False positive rate ROC curves. The findings can be used by financial institutions to develop and enhance fraud detection strategies as they demonstrate the effectiveness and reliability of the proposed approach. This study emphasizes the critical role that innovative technologies play in safeguarding the financial sector from the ever-changing strategies of fraudsters while also enhancing banking security. • This paper aims to implement the detection of fraudulent transactions using a state-of-the-art Graph Database approach. • The relational graph of features in the dataset used is modelled using Neo4J as a graph database. • Applying JSON features from the exported graph to various Machine Learning models, giving effective outcomes.

With recent advancements in the classification methods of various domains, deep learning has shown remarkable results over traditional neural networks. A compact convolutional neural network (CNN) model with reduced computational complexity that performs equally well compared to the pretrained ResNet-101 model was developed. This three-layer CNN model was developed for plant leaf classification in this work. The classification of disease in tomato plant leaf images of the healthy and disease classes from the PlantVillage (PV) database is discussed in this work. Further, it supports validating the models with the images taken at “Krishi Vigyan Kendra Narayangaon (KVKN),” Pune, India. The disease categories were chosen based on their prevalence in Indian states. The proposed approach presents a performance improvement concerning other state-of-the-art methods; it achieved classification accuracies of 99.13%, 99.51%, and 99.40% with N1, N2, and N3 models, respectively, on the PV dataset. Experimental results demonstrate the validity of the proposed approach under complex background conditions. For the images captured at KVKN for predicting tomato plant leaf disease, the validation accuracy was 100% for the N1 model, 98.44% for the N2 model, and 96% for the N3 model. The training time for the developed N2 model was reduced by 89% compared to the ResNet-101 model. The models developed are smaller, more efficient, and less time-complex. The performance of the developed model will help us to take a significant step towards managing the infected plants. This will help farmers and contribute to sustainable agriculture.