Deep Learning for Sorghum Yield Forecasting using Uncrewed Aerial Systems and Lab-Derived Imagery

Abstract

The AI revolution, advanced Graphics Processing Units (GPUs), and open-source platforms have enabled Machine Learning (ML) and Deep Learning (DL) algorithms to rapidly and accurately extract phenotypic features from Uncrewed Aerial System (UAS)-derived imagery. Such advancement leads to phenotypic digitization and sorghum yield forecasting. Yield analytics are critical for breeding programs to assess the genetics and breeding potential of genotypes to enhance cultivar development. This trial followed a three-replicated Randomized Complete Block Design (RCBD) with 36 diverse sorghum genotypes in 2023 at Ashland Bottoms, Kansas. The field images were captured 6 meters above using a DJI M300 drone equipped with the P1 sensor at nadir (90 degrees) and oblique (45 degrees) angles. This research trained YOLO and the Faster R-CNN (Detectron2) models to harness yield attributes from UAS field and lab images. The YOLO models outperformed the Faster R-CNN model in detecting sorghum panicles, achieving a mean average precision at 50% Intersection over Union (IoU) ranging from 0.92 to 0.98, compared to 0.61 to 0.89. Panicle detection from field imagery correlated at 0.86 with ground truth. Lab imagery analyses measured panicle size, seed counts, and seed area with correlation coefficients of 0.71, 0.95, and 0.25, respectively. Three machine learning models: Support Vector Regression (SVR), Decision Tree Regression (DTR), and Random Forest Regression (RFR) are used to predict yield with correlation coefficients of 0.58, 0.76, and 0.70, respectively. We observed that YOLO models are well-suited for extracting yield-attributing traits from images, which are then incorporated into ML regression models to improve yield prediction performance.

Plant phenotyping relevance

UAS・実験室画像からソルガム穂の検出、サイズ・種子数・面積などの形質抽出と収量予測を行い、複数の物体検出モデルを比較検証しているため、表現型取得手法が中心である。

Code and data availability

The supplied blocks describe UAS/lab imagery, YOLO/Detectron2 models, and ML yield forecasting, but contain no data or code availability statement, no public repository, and no author-provided URL for datasets, images, trained models, or analysis scripts. Only generic third-party tools (Roboflow, Ultralytics, Detectron

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