Deep Learning-based Object Detection for Crop Monitoring in Soybean Fields

Pratama, Muhammad Taufiq; Kim, Sang–Wook; Ozawa, Seiichi; Ohkawa, Takenao; Chona, Yuya; Tsuji, Hiroyuki; Murakami, Noriyuki

doi:10.1109/ijcnn48605.2020.9207400

Cited by 12 publications

(3 citation statements)

References 14 publications

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“…The RetinaNet model achieved higher accuracy for wheat spikes at the filling and maturity stages. Compared to Faster R-CNN and RetinaNet, Cascade R-CNN obtained a higher average precision (AP) of 89.6 for the detection and counting of soybean flowers and seeds [42]. You only look once (YOLO)v4 architecture was used to improve the detection speed and accuracy of wheat spikes [43].…”

Section: Crop Organ Detection and Countingmentioning

confidence: 99%

Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review

Wang

2022

Agronomy

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Computer vision (CV) combined with a deep convolutional neural network (CNN) has emerged as a reliable analytical method to effectively characterize and quantify high-throughput phenotyping of different grain crops, including rice, wheat, corn, and soybean. In addition to the ability to rapidly obtain information on plant organs and abiotic stresses, and the ability to segment crops from weeds, such techniques have been used to detect pests and plant diseases and to identify grain varieties. The development of corresponding imaging systems to assess the phenotypic parameters, yield, and quality of crop plants will increase the confidence of stakeholders in grain crop cultivation, thereby bringing technical and economic benefits to advanced agriculture. Therefore, this paper provides a comprehensive review of CNNs in computer vision for grain crop phenotyping. It is meaningful to provide a review as a roadmap for future research in such a thriving research area. The CNN models (e.g., VGG, YOLO, and Faster R-CNN) used CV tasks including image classification, object detection, semantic segmentation, and instance segmentation, and the main results of recent studies on crop phenotype detection are discussed and summarized. Additionally, the challenges and future trends of the phenotyping techniques in grain crops are presented.

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Section: Crop Organ Detection and Countingmentioning

confidence: 99%

Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review

Wang

2022

Agronomy

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“…Advances in statistical learning theories and application programming interface packages for machine learning (ML) have enabled data assimilation and feature identification for plant phenotyping. ML approaches play key roles in plant HTP, such as detecting corn kernels using convolutional neural networks (CNN) and digital red-green-blue images (Khaki et al, 2020 ) and identifying soybean flowers and seedpods using region-based CNN (RCNN) (Pratama et al, 2020 ). ML approaches have also been used to predict plant traits, such as yield by CNN (Zhou et al, 2021b ), maturity date by partial least square regression (Zhou et al, 2019 ) and stress responses by artificial neural networks, decision tree models, linear discriminant analysis, and support vector machine (Bai et al, 2018 ; Zhou et al, 2020 , 2021a ).…”

Section: Introductionmentioning

confidence: 99%

Improve Soybean Variety Selection Accuracy Using UAV-Based High-Throughput Phenotyping Technology

et al. 2022

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The efficiency of crop breeding programs is evaluated by the genetic gain of a primary trait of interest, e.g., yield, achieved in 1 year through artificial selection of advanced breeding materials. Conventional breeding programs select superior genotypes using the primary trait (yield) based on combine harvesters, which is labor-intensive and often unfeasible for single-row progeny trials (PTs) due to their large population, complex genetic behavior, and high genotype-environment interaction. The goal of this study was to investigate the performance of selecting superior soybean breeding lines using image-based secondary traits by comparing them with the selection of breeders. A total of 11,473 progeny rows (PT) were planted in 2018, of which 1,773 genotypes were selected for the preliminary yield trial (PYT) in 2019, and 238 genotypes advanced for the advanced yield trial (AYT) in 2020. Six agronomic traits were manually measured in both PYT and AYT trials. A UAV-based multispectral imaging system was used to collect aerial images at 30 m above ground every 2 weeks over the growing seasons. A group of image features was extracted to develop the secondary crop traits for selection. Results show that the soybean seed yield of the selected genotypes by breeders was significantly higher than that of the non-selected ones in both yield trials, indicating the superiority of the breeder's selection for advancing soybean yield. A least absolute shrinkage and selection operator model was used to select soybean lines with image features and identified 71 and 76% of the selection of breeders for the PT and PYT. The model-based selections had a significantly higher average yield than the selection of a breeder. The soybean yield selected by the model in PT and PYT was 4 and 5% higher than those selected by breeders, which indicates that the UAV-based high-throughput phenotyping system is promising in selecting high-yield soybean genotypes.

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“…A soybean flower/seedpod detection system was built to collect growing state data by introducing convolutional neural networks. In this method, observed plant states (e.g., #flowers and #seedpods), in combination with predicted future environmental data, are used to predict soybean crop yields ( Pratama et al, 2020 ). Lu et al (2022) proposed a soybean yield in-field prediction method based on bean pods and leaf image recognition using a deep learning algorithm, combined with a generalized regression neural network (GRNN).…”

Section: Introductionmentioning

confidence: 99%

Automatic and Accurate Acquisition of Stem-Related Phenotypes of Mature Soybean Based on Deep Learning and Directed Search Algorithms

et al. 2022

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The stem-related phenotype of mature stage soybean is important in soybean material selection. How to improve on traditional manual methods and obtain the stem-related phenotype of soybean more quickly and accurately is a problem faced by producers. With the development of smart agriculture, many scientists have explored soybean phenotypes and proposed new acquisition methods, but soybean mature stem-related phenotype studies are relatively scarce. In this study, we used a deep learning method within the convolutional neural network to detect mature soybean stem nodes and identified soybean structural features through a novel directed search algorithm. We subsequently obtained the pitch number, internodal length, branch number, branching angle, plant type spatial conformation, plant height, main stem length, and new phenotype-stem curvature. After 300 epochs, we compared the recognition results of various detection algorithms to select the best. Among them, YOLOX had a maximum average accuracy (mAP) of 94.36% for soybean stem nodes and scale markers. Through comparison of the phenotypic information extracted by the directed search algorithm with the manual measurement results, we obtained the Pearson correlation coefficients, R, of plant height, pitch number, internodal length, main stem length, stem curvature, and branching angle, which were 0.9904, 0.9853, 0.9861, 0.9925, 0.9084, and 0.9391, respectively. These results show that our algorithm can be used for robust measurements and counting of soybean phenotype information, which can reduce labor intensity, improve efficiency, and accelerate soybean breeding.

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Deep Learning-based Object Detection for Crop Monitoring in Soybean Fields

Cited by 12 publications

References 14 publications

Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review

Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review

Improve Soybean Variety Selection Accuracy Using UAV-Based High-Throughput Phenotyping Technology

Automatic and Accurate Acquisition of Stem-Related Phenotypes of Mature Soybean Based on Deep Learning and Directed Search Algorithms

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