Utilizing UAV-based multispectral imagery and convolutional neural networks for brix value prediction

Sugarcane is one of the major crops cultivated in tropical and subtropical regions worldwide. Assessing crop maturity is important for optimizing harvest timing and improving yield. Conventional sugarcane maturity evaluations use agronomic characteristics, past trends, and eye inspections, which are labor-intensive and not precise, particularly over large plantations. Some sugarcane varieties mature to complete ripeness earlier than their expected maturity age, rendering physical observation inefficient and unsuitable. To address this point, this experimental research study introduces a novel, cost-effective approach using Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors to estimate sugarcane maturity through remote sensing and deep learning techniques. The primary objective is to develop an efficient deep learning-based classification system for identifying mature sugarcane fields from multispectral images gathered using UAVs. Pelwatte Lanka Sugar Company (Pvt) Ltd geo-referenced yield data were used together with multispectral imagery of 3–12-month-old plant-crop sugarcane fields from intermediate and dry regions. Fields were classed as ‘matured’ (Brix > 10) or ‘immatured’ (Brix ≤ 10) based on mean Brix values. Red, Red Edge, Green, Near-Infrared (NIR), and spectral bands and vegetation indices NDVI and NDRE were investigated. 17,256 images with a resolution of 200×200 pixels were utilized (2,876 for each band/index). The dataset was split between training and validation sets. Modeling was done in two phases: (1) comparison of the feature extractor and (2) constructing a specific Convolutional Neural Network (CNN). The proposed CNN achieved a maximum accuracy of 93% on NIR images, whereas Red, Green, Red Edge, NDVI, and NDRE achieved 84%, 81%, 76%, 69%, and 59% accuracy, respectively. The results indicated that the model can classify sugarcane maturity with a high level of accuracy, thus improving precision agriculture methods.