A modular artificial neural network technique for early estimation of cotton yield using unmanned aerial system

Abstract

Early crop yield estimation aids growers of cotton and other crops in making in-season management decisions and estimating the crop's market value. However, predicting yield early in the growing season is challenging for various reasons including environmental factors and soil variability. Several techniques have been used to estimate cotton yield, and in the last decade machine learning (especially artificial neural networks or ANNs) have been widely adopted. In a standard ANN model, all the input data are collated without considering the temporal characteristics of the data, such as when the data were collected relative to the growth stage of the plants. A modular network called plus artificial neural network (ANN+) was devised to independently receive crop data that has been collected at different stages of crop growth. This study evaluated the potential of adopting ANN+ for early estimation of cotton yield. For this purpose, a field experiment was conducted in central Texas in e2020 and 2021. The study site consisted of three different treatments: variable nitrogen rate, variable fertilizer and irrigation × variety. An unmanned aerial vehicle equipped (UAV) with a fiveband multispectral sensor was flown at various cotton growth stages to collect remote sensing data multiple times within 100 days after planting. The UAV was flown at 30 m above ground level, producing a spatial resolution of approximately 0.02 m. The multispectral imagery was used to extract crop spectral, textural and structural information. Along with this information, weather information in term of growing degree days, solar insolation and precipitation were collected for yield estimation. The custom ANN+ model achieved an R 2 of 0.90 and mean absolute percentage error of 12.29% for cotton yield estimation. Seasonal temperature data contributed the most information to the model, but crop structural and textural metrics from the image data also contributed strongly to the model, suggesting that autonomous aerial systems can be an important part of providing cotton growers early predictions of yield.