Cotton Yield Estimation From Aerial Imagery Using Machine Learning Approaches.

Abstract

Estimation of cotton yield before harvest offers many benefits to breeding programs, researchers and producers. Remote sensing enables efficient and consistent estimation of cotton yields, as opposed to traditional field measurements and surveys. The overall goal of this study was to develop a data processing pipeline to perform fast and accurate pre-harvest yield predictions of cotton breeding fields from aerial imagery using machine learning techniques. By using only a single plot image extracted from an orthomosaic map, a Support Vector Machine (SVM) classifier with four selected features was trained to identify the cotton pixels present in each plot image. The SVM classifier achieved an accuracy of 89%, a precision of 86%, a recall of 75%, and an F1-score of 80% at recognizing cotton pixels. After performing morphological image processing operations and applying a connected components algorithm, the classified cotton pixels were clustered to predict the number of cotton bolls at the plot level. Our model fitted the ground truth counts with an R 2 value of 0.93, a normalized root mean squared error of 0.07, and a mean absolute percentage error of 13.7%. This study demonstrates that aerial imagery with machine learning techniques can be a reliable, efficient, and effective tool for pre-harvest cotton yield prediction.

Plant phenotyping relevance

航空画像と機械学習による綿花の収量・果球数推定パイプラインを開発し、画素分類と地上計数で性能検証しており、植物表現型の取得・抽出が研究の中心である。

Code and data availability

The paper's cotton boll classification/counting pipeline is publicly available: a Dockerized web app on Docker Hub and code with sample test images on GitHub, both explicitly stated by the authors. Raw aerial imagery/ground truth data are only available on request.