Yield Estimation in Banana Orchards Based on DeepSORT and RGB-Depth Images

Abstract

Orchard yield estimation is one of the key indicators of precision agriculture. The traditional random sampling yield estimation method has strict requirements for the laborer experience and scale of orchards. Intelligent orchard management enables growers to use resources more effectively and make wiser decisions to optimize orchard inputs. This study proposes a banana bunch counting and yield estimation method based on the DeepSORT tracking algorithm. This method involves obtaining RGB-D images and calculating the weight of an individual bunch of bananas, which was promoted in our previous work. Building on this, the DeepSORT was used to solve the repeated counting based on the Hungarian algorithm and Kalman filtering. Three constraints were set to improve the statistical accuracy, and a yield estimation system was designed for orchard management monitoring. This system provides managers with bunch weight predictions and statistical plant information to achieve real-time yield estimations for banana orchards. The experimental results showed that the accuracy of the yield estimations reached 97.25% and that banana bunch counting had a success rate of 96.82%. This demonstrates that the effective integration of RGB-D technology and the DeepSORT algorithm can be successfully applied to the intelligent management and harvesting of banana orchards.

Plant phenotyping relevance

RGB-D画像とDeepSORTによるバナナ房の計数・重量推定という、植物の収量形質を抽出する画像ベース手法が研究の中心であり、精度評価も実施しているため。

Code and data availability

The paper's RGB-D banana orchard data and phenotyping/yield-estimation assets are not publicly deposited; the Data Availability Statement says they are available only on request from the corresponding author. No public code, dataset, or model URL is provided.

No evidence-backed public reproduction asset is currently recorded.