A Service-based Joint Model Used for Distributed Learning: Application for Smart Agriculture

Abstract

Distributed analytics facilitate to make the data-driven services smarter for a wider range of applications in many domains, including agriculture. The key to producing services at such level is timely analysis for deriving insights from reliable data. Centralized data analytic services are becoming infeasible due to limitations in the Information and Communication Technologies (ICT) infrastructure, timeliness of the information, and data ownership. Distributed Machine Learning (DML) platforms facilitate efficient data analysis and overcome such limitations effectively. Federated Learning (FL) is a DML methodology, which enables optimizing resource consumption while performing privacy-preserved timely analytics. In order to create such services through FL, there needs to be innovative machine learning (ML) models as data complexity as well as application requirements limit the applicability of existing ML models. Even though NN-based models are highly advantageous, use of NN in FL settings is limited with thin clients (with less computational capabilities) and high-dimensional data (with large number of model parameters). Therefore, in this paper, we propose a novel Neural Network (NN)and Partial Least Square (PLS) regression-based joint FL model (FL-NNPLS). Its predictive performance is evaluated under sequentialand parallel-updating based FL algorithms in a smart farming context for milk quality analysis. Smart farming is a fast-growing industrial sector which requires effective analytics platforms to enable sustainable farming practices. However, the use of advanced ML techniques are still at a early stage for improving the effectiveness of farming practices. Our FL-NNPLS approach performs and compares well with a centralized approach and demonstrates state-of-the-art performance.

FedAAR: A Novel Federated Learning Framework for Animal Activity Recognition with Wearable Sensors

“…To simulate practical scenarios, i.e., data heterogeneity across farms (clients), we assigned the horse dataset to multiple clients according to individuals since the movement patterns of individual animals are often drawn from distinct distributions. This kind of data allocation refers to some previous works [ 6 , 15 , 34 ], which generally divide a single centralised dataset into small ones. In addition, we separately selected one horse dataset (excluded in the training set) as the test set to test the trained model’s performance in each run of the experiment, which effectively promotes the generalisation capabilities of the model.…”

Towards Digital Innovation: Stakeholder Interactions in Agricultural Data Ecosystem in Croatia

“…This stakeholder analysis results should prove valuable in developing collaboration, joint projects, or policies, but also in solving sectoral business problems where the participatory approach is required [24]. In the further maturation of the agricultural data ecosystem in Croatia, both for the open governmental data and the data of the public endeavours as well as with developing the contractual sharing and the effective data governance, the critical findings of the underdeveloped relationships, need for better data supply should be taken into account [7][8][9][10][11][12][13][14]. Despite numerous initiatives for cooperation and data sharing between stakeholders in public and private sector at different levels, limited impact to sustainable value creation has been achieved in industries including agriculture, and unsustainable practices persist [14,68,69].…”

“…Therefore, the integration of the following nine aspects: cooperation; inclusion; financing; diversification; communication; policies; knowledge with entrepreneurship; and production enables the creation of sustainable values. The key tool for the integration of these aspects is the data sharing (either as re-using of the open data or as contractual sharing), enabled by the effective data governance [7][8][9][10][11][12][13][14].…”

Intelligent Agriculture Decision System Based on Machine Learning

“…In general, after confirming the input variables and prediction variables, select appropriate functional models according to their characteristics, compare, analyze and evaluate the performance of each model, and select the best model as the final prediction model. Since precipitation is a nonlinear and non-stationary process, which is interfered by many factors, the prediction accuracy of data directly used for function modeling will be affected to some extent [15][16].…”