This scientific article analyzes the practical significance of scientific innovations identified as a result of research conducted on the topic "Justification of the efficiency and flight-technical parameters of aircraft in agricultural work." In this case, the mathematical model of the effectiveness of agroaviation work, the assessment of the influence of flight parameters on efficiency through a multifactorial correlation model, and the forecast of the results achieved in 2025-2030 are highlighted. The influence of flight parameters on aircraft performance has been scientifically substantiated through a multifactorial correlation model, such as flight speed, operating altitude, payload weight, and operating time.

Also, the article fully explains the correlation and regression models corresponding to the research direction and graphically depicts the growth dynamics of the agroaviation efficiency index (ASI) in 2025-2030 through practical calculations.  

Agroaviation, efficiency of agroaviation work, flight-technical parameters, multifactorial correlation model

Sources on agro-aviation and agricultural aviation

Thrush Aircraft Inc. Agricultural Aviation Handbook. Georgia, USA.

Johnson, D. R., & Barry, J. W. Principles of Agricultural Aerial Application. USDA, 2012.

Teske, M. E., Bird, S. L. AgDRIFT Model: Spray Drift Simulation Model. USDA Forest Service.

Mattingly, J. D. Aircraft Performance: Theory and Practice. AIAA Education Series.

Statistical analysis, correlation and regression models

Montgomery, D. C., Peck, E. A., & Vining, G. G. *Introduction to Linear Regression Analysis*. Wiley, 2021.

Draper, N. R., & Smith, H. Applied Regression Analysis. Wiley, 2014.

Gujarati, D. N., & Porter, D. C. Basic Econometrics. McGraw-Hill.

Kutner, M. H., Nachtsheim, C., & Neter, J. Applied Linear Statistical Models. McGraw-Hill.

Resources for intelligent modeling and optimization

Russell, S., & Norvig, P. Artificial Intelligence: A Modern Approach. Pearson, 2021.

Haykin, S. Neural Networks and Learning Machines. Pearson, 2013.

Simon, D. *Optimal State Estimation: Kalman, H. *Infinity and Nonlinear Approaches*. Wiley, 2006.

Bishop, C. M. Pattern Recognition and Machine Learning. Springer.

Deb, K. Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, 2001.

Agroengineering, technical parameters and modeling of aircraft

Rosell, J. R., Sanz, R. Advances in Precision Agriculture Technologies. Springer.

Raymer, D. P. Aircraft Design: A Conceptual Approach. AIAA, 2018.

Anderson, J. D. Introduction to Flight. McGraw-Hill.

Zhang, C., & Kovacs, J. The Application of Small Unmanned Aerial Systems for Precision Agriculture: A Review. Precision Agriculture.

Agroaviation efficiency, forecasting and digital management

Li, W., et al. Prediction models for UAV-based Agricultural Spraying. Computers and Electronics in Agriculture.

Boedeker, G., et al. Model-Based Decision Support in Precision Agriculture. Elsevier, 2020.