The contemporary agricultural market is profoundly volatile, where agricultural prices are based on a complex supply chain, climatic irregularity or unscheduled market demand. Prices of crops need to be predicted in a reliable and timely manner for farmers, policy-makers and other stakeholders to take evidence-based decisions ultimately for the benefit towards sustainable agriculture and economic sustainability. Objective: The objective of this study is to develop and evaluate a comprehensive machine learning model for predicting agricultural prices incorporating logistic, economic and environmental considerations. It is the desire to make agriculture more profitable by building simple and accurate forecasting models. Methods: An assorted dataset was collected, which covers major factors to constitute the dataset of temperature, rainfall, fertiliser use, pest and disease attack level, cost of transportation, market demand-supply ratio and regional competitiveness. The data was subjected to pre-processing and feature extraction for quality control/quality assurance. Several machine learning models (Linear Regression, Support Vector Machines, AdaBoost, Random Forest, and XGBoost) were trained and evaluated using performance metrics such as R2 score, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Results: Out of the model approaches that were analysed, predictive performance was superior for XGBoost (with an R2 Score of 0.94, RMSE of 12.8 and MAE of 8.6). To generate accurate predictions, the ability to account for complex non-linear relationships between market and environmental information was necessary. Conclusions: The forecast model of the XGBoost-based prediction system is reliable, of low complexity and widely applicable to large-scale real-time forecasting of agricultural monitoring. The model substantially reduces the uncertainty of price forecasting, and does so by including multivariate environmental and economic aspects that permit more profitable management practices in a schedule for future sustainable agriculture.

Background: The need for autonomous aerial surveillance originates from weaknesses in manual monitoring, such as late response, low scalability and rigid patrol plans. AI and GPS-driven smart aerial monitoring present an attractive solution for continuous adaptive wide-area surveillance. Objective: In this paper, we aim at designing and validating experimentally a low-cost drone-based unmanned autonomous mission patrolling system with waypoint navigation, real-time video backhauling, AI-based human/object detection and GPS path re-planning when an event occurs to ensure the safety of patrol missions under battery constraints. Methods: The proposed architecture combines autonomous navigation and embedded flight-control with online analog video streaming and ground-station-based computer vision processing. Object detection based on deep learning for live aerial video is used, and the proposed system’s performance is tested at different altitudes, lighting states and GPS patrol plans. Results: Experimental results show that the proposed method can obtain stable waypoint tracking with a clear real-time video downlink in patrol missions. The system is able to adaptively modify paths as a reaction to detected events and commence safe return-to-home functionality during low-battery conditions. The proposed detection model obtains a mean average precision of 87.4%, with an F1-score of 0.89 and real-time inference latency (20–25 ms per frame) that enables fast service without any interruption in practice during surveillance deployment. Conclusions: Experimental results show that the proposed method can obtain stable waypoint tracking with a clear real-time video downlink in patrol missions. The system can adaptively modify paths as a reaction to detected events and commence safe return-to-home functionality during low-battery conditions. The proposed detection model obtains a mean average precision of 87.4%, with an F1-score of 0.89 and real-time inference latency (20–25 ms per frame) that enables fast service without any interruption in practice during surveillance deployment.

An Artificial Neural Network (ANN) is a data preparing method that is motivated by the way organic sensory systems, for example, the mind, process data. ANN learns (i.e. logically enhance execution on) errands by thinking about illustrations, for the most part without assignment particular programming. Backpropagation is a method utilized as a part of neural networks to figure a value that is required in the computation of the weights to be utilized as a part of the system. In this project, neural network Backpropagation with adaptive learning rate is used to predict heart and diabetes diseases to the patients by the given parameters. The algorithm modifies the learning rate at each step based on the data and gives faster results when compared to the traditional algorithm. This is chosen to implement on Hear Diseases and Diabetic Disease dataset and results are compared. The model is trained with a set of training data and then that trained model is used to predict the testing data of patient disease. These predictions will give an idea of the type of disease and can be used to diagnose the patient.

Big data stands for huge set or collecting information which can't be prepared by modern techniques, for example, data processing. Ex- mining enormous information has the quality in the arena of interpersonal organizations, spot business patterns, web, drug, science, fund, commerce informatics and indeed in government. Dissecting information would assistance in extraordinary basic leadership, which may achieve change happening productivity, diminishment in cost and disappointment dangers. Enormous information examination turns into an awesome hunger for the creating associations since it winds up plainly troublesome for those associations to process a excess of tera bytes of information. Huge information investigation even discovers its request in considerate the purpose behind regular or man influenced failures by gathering enormous information keeping in observance conclusion to recuperate from the catastrophe and to develop the correspondence since correspondence is the primary test that the common people face while confronting sudden failures..