ISCF: A Lightweight Deep Learning Framework for Automated Indoor Counting of Soybean Pods and Seeds

Abstract

Highlights This article proposes ISCF, a novel method for precise soybean pod and seed counting using a segmentation followed by a classification strategy. Compared to YOLO, ISCF offers faster inference, higher accuracy, and a more efficient pipeline for real-time applications. The proposed method focuses on the practical value of the lightweight design, making it deployable on edge devices for real-world use. The proposed method applies to the automated counting of seeds or fruits of various crops in controlled indoor environments, demonstrating strong generalizability and adaptability. Abstract. Accurate counting of soybean pods and seeds is essential for yield prediction, crop management, and variety improvement. However, existing automatic methods under controlled indoor conditions often exhibit limited computational efficiency, insufficient accuracy, and limited practical deployment for reducing manual workload. To address this, we propose an Indoor Soybean Counting Framework (ISCF), a lightweight deep learning framework that decouples localization and classification into two independent stages to count soybean pods and seeds. ISCF first performs precise segmentation of soybean pods using the proposed Indoor Soybean Segmentation Network (ISSN), followed by classification of the number of seeds per pod using a MobileNetV3-based architecture. Optimized for lightweight design, ISCF is well-suited to real-time deployment on edge devices. Experimental results demonstrate the superior performance of ISCF in soybean pod and seed counting tasks, achieving an AP 50 of 99.5% for pod segmentation, a mean absolute error (MAE) of merely 0.72, and an R 2 of 0.9942 for pod counting, and an MAE of 3.79 and an R 2 of 0.9573 for seed counting. Moreover, ISCF generalizes well to datasets from four additional crop species, underscoring its potential for a broad range of indoor crop counting and phenotyping applications. Keywords: Image classification, Image recognition, Instance segmentation, Lightweight network, Plant phenotyping, Soybean counting.

Plant phenotyping relevance

植物の莢・種子数という収量関連形質を画像から自動抽出する軽量深層学習フレームワークを開発・評価しており、表現型取得手法が中心である。

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