Multispectral Approach for Wheat Yield Estimation Using Deep Learning

Abstract

Automation is becoming increasingly vital across various professions and domains, including agricultural practices. Remote-sensing-based wheat yield estimation has emerged as a superior alternative to traditional yield prediction methods. Historically, wheat yield measurement involved labor-intensive and time-consuming destructive sampling techniques. However, accurate and timely yield forecasts are pivotal for decision-making processes such as crop harvesting plans, milling, marketing, and forward selling strategies, thereby enhancing the efficiency and profitability of the global wheat sector. Presently, producers or productivity officers, often funded by mills, rely on destructive or visual sampling techniques to assess wheat production during the growing season. There's a growing demand for swift and efficient problem-solving methods. Consequently, the adoption of machinery for wheat cultivation has surged, aiming to lower production costs, reduce labor demands on farmers, and enhance harvest efficiency. Although not extensively compared, existing techniques for estimating agricultural output typically employ regression models relying on specific forecasting factors. This study aims to illustrate and compare the effectiveness of utilizing satellite earth observation data for monitoring agriculture, particularly in wheat production. Multiple regression models are compared, utilizing various predictor variables. The study incorporates wheat yield estimation techniques, such as regression models, time series analysis of vegetation indices, remote sensing, phenology measurements, and normalized difference vegetation index (NDVI). Artificial intelligence algorithms, including Random Forest and ordinary least squares, are employed to develop a suggested approach that accurately correlates ground-measured data. This research introduces a novel wheat yield estimation technique, which significantly improves forecasting accuracy and holds promise for enhancing decision-making processes in wheat farming practices.