Soybean cyst nematode detection and management: a review

Abstract

Soybeans play a key role in global food security. U.S. soybean yields, which comprise $$32\%$$ 32 % of the total soybeans planted in the world, continue to experience unprecedented grain loss due to the soybean cyst nematode (SCN) plant pathogen. SCN remains one of the primary disruptive pests despite the existence of advanced management techniques such as crop rotation and SCN-resistant varieties. SCN detection is a key step in managing this disease; however, early detection is challenging because soybeans do not show any above ground symptoms unless they are significantly damaged. Direct soil sampling remains the most common method for SCN detection, however, this method has several problems. For example, the threshold damage methods—adopted by most of the laboratories to make recommendations—is not reliable as it does not consider soil pH, N, P, and K values and relies solely on the egg count instead of assessment of the root infection. To overcome the challenges of manual soil sampling methods, deep learning and hyperspectral imaging are important current topics in precision agriculture for plant disease detection and have been proposed as cost-effective and efficient detection methods that can work at scale. We have reviewed more than 150 research papers focusing on soybean cyst nematodes with an emphasis on deep learning techniques for detection and management. First: we describe soybean vegetation and reproduction stages, SCN life cycles, and factors influencing this disease. Second: we highlight the impact of SCN on soybean yield loss and the challenges associated with its detection. Third: we describe direct sampling methods in which the soil samples are procured and analyzed to evaluate SCN egg counts. Fourth: we highlight the advantages and limitations of these direct methods, then review computer vision- and remote sensing-based detection methods: data collection using ground, aerial, and satellite approaches followed by a review of machine learning methods for image analysis-based soybean cyst nematode detection. We highlight the evaluation approaches and the advantages of overall detection workflow in high-performance and big data environments. Lastly, we discuss various management approaches, such as crop rotation, fertilization, SCN resistant varieties such as PI 88788, and SCN’s increasing resistance to these strategies. We review machine learning approaches for soybean crop yield forecasting as well as the influence of pesticides, herbicides, and fertilizers on SCN infestation reduction. We provide recommendations for soybean research using deep learning and hyperspectral imaging to accommodate the lack of the ground truth data and training and testing methodologies, such as data augmentation and transfer learning, to achieve a high level of detection accuracy while keeping costs as low as possible.

Current advances in the identification of plant nematode diseases: From lab assays to in-field diagnostics

“…Biological image datasets for multiple genera of PPNs were established and used to identify them based on the deep convolutional neural networks (CNNs) method (Lu et al, 2021). A convolutional CNNs model for identification of nematodes in soybean crop was developed by Abade et al (2022).AI or Deep learning combined with hyperspectral image analysis is more popular because of the advantages this assay presents over direct soil methods (Arjoune et al, 2022). A combination of infrared spectra analysis and AI assay was used to detect rootknot nematode M. enterorlobii at the early stage of infection (San-Blas et al, 2020).…”

The Novel ML Approaches on Wheat Disease by CLF and FOHF of Image Equalization Techniques

“…[22] The majority of attributes, including colour, texture, and shape, are extracted without taking into account the bending and tilting of crop leaves, which could easily compromise the recognition impact. [24,27,28]. Zhang et al [23] outlined an enhanced Ir-UNet technique for wheat yellow rust detection that incorporated an irregular encoder module, an irregular decoder module, and a content-aware channel re-weight module.…”

Glycine maxpolygalacturonase inhibiting protein 11 (GmPGIP11) functions in the root to suppressHeterodera glycinesparasitism

“…Among the most devastating PPNs is Heterodera glycines , the soybean cyst nematode (SCN), found globally where soybeans are grown in more than 50 countries (Arjoune et al 2022). H. glycines arrived in the U.S. in 1954, and now causes 36% of all losses by pathogens in the U.S., estimated at 1.5 billion U.S. dollars (Wrather et al 2001; Arjoune et al 2022).…”