Agronomy

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23 pages, 7522 KiB

Open AccessArticle

Scalable Prediction of Northern Corn Leaf Blight and Gray Leaf Spot Diseases to Predict Fungicide Spray Timing in Corn

by Layton Peddicord, Alencar Xavier, Steven Cryer, Jeremiah Barr and Gerie van der Heijden

Agronomy 2025, 15(2), 328; https://doi.org/10.3390/agronomy15020328 - 27 Jan 2025

Viewed by 905

Managing foliar corn diseases like northern leaf blight (NLB) and gray leaf spot (GLS), which can occur rapidly and impact yield, requires proactive measures including early scouting and fungicides to mitigate these effects. Decision support tools, which use data from in-field monitors and [...] Read more.

Managing foliar corn diseases like northern leaf blight (NLB) and gray leaf spot (GLS), which can occur rapidly and impact yield, requires proactive measures including early scouting and fungicides to mitigate these effects. Decision support tools, which use data from in-field monitors and predicted leaf wetness duration (LWD) intervals based on meteorological conditions, can help growers to anticipate and manage crop diseases effectively. Effective crop disease management programs integrate crop rotation, tillage practices, hybrid selection, and fungicides. However, growers often struggle with correctly timing fungicide applications, achieving only a 30–55% positive return on investment (ROI). This paper describes the development of a disease-warning and fungicide timing system, equally effective at predicting NLB and GLS with ~70% accuracy, that utilizes historical and forecast hourly weather data. This scalable recommendation system represents a valuable tool for proactive, practicable crop disease management, leveraging in-season weather data and advanced modeling techniques to guide fungicide applications, thereby improving profitability and reducing environmental impact. Extensive on-farm trials (>150) conducted between 2020 and 2023 have shown that the predicted fungicide timing out-yielded conventional grower timing by 5 bushels per acre (336 kg/ha) and the untreated check by 9 bushels per acre (605 kg/ha), providing a significantly improved ROI. Full article

(This article belongs to the Special Issue Tools and Techniques for Monitoring Pests and Diseases in Agro-Ecosystem)

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15 pages, 2776 KiB

Open AccessArticle

Research on a Target Detection Algorithm for Common Pests Based on an Improved YOLOv7-Tiny Model

by He Gong, Xiaodan Ma and Ying Guo

Agronomy 2024, 14(12), 3068; https://doi.org/10.3390/agronomy14123068 - 23 Dec 2024

Cited by 1 | Viewed by 876

Abstract

In agriculture and forestry, pest detection is critical for increasing crop yields and reducing economic losses. However, traditional deep learning models face challenges in resource-constrained environments, such as insufficient accuracy, slow inference speed, and large model sizes, which hinder their practical application. To [...] Read more.

In agriculture and forestry, pest detection is critical for increasing crop yields and reducing economic losses. However, traditional deep learning models face challenges in resource-constrained environments, such as insufficient accuracy, slow inference speed, and large model sizes, which hinder their practical application. To address these issues, this study proposes an improved YOLOv7-tiny model designed to deliver efficient, accurate, and lightweight pest detection solutions. The main improvements are as follows: 1. Lightweight Network Design: The backbone network is optimized by integrating GhostNet and Dynamic Region-Aware Convolution (DRConv) to enhance computational efficiency. 2. Feature Sharing Enhancement: The introduction of a Cross-layer Feature Sharing Network (CotNet Transformer) strengthens feature fusion and extraction capabilities. 3. Activation Function Optimization: The traditional ReLU activation function is replaced with the Gaussian Error Linear Unit (GELU) to improve nonlinear expression and classification performance. Experimental results demonstrate that the improved model surpasses YOLOv7-tiny in accuracy, inference speed, and model size, achieving a MAP@0.5 of 92.8%, reducing inference time to 4.0 milliseconds, and minimizing model size to just 4.8 MB. Additionally, compared to algorithms like Faster R-CNN, SSD, and RetinaNet, the improved model delivers superior detection performance. In conclusion, the improved YOLOv7-tiny provides an efficient and practical solution for intelligent pest detection in agriculture and forestry. Full article

(This article belongs to the Special Issue Tools and Techniques for Monitoring Pests and Diseases in Agro-Ecosystem)

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12 pages, 1290 KiB

Open AccessArticle

UV Fluorescent Powders as a Tool for Plant Epidemiological Studies

by Paul M. Severns, Clarence Codod and Ashley J. Lynch

Agronomy 2024, 14(10), 2405; https://doi.org/10.3390/agronomy14102405 - 17 Oct 2024

Viewed by 786

Abstract

Some basic aspects of plant disease epidemiology remain largely unknown due to a lack of empirical study methods to experimentally manipulate the position of infections within a single plant or within a plant canopy and the dispersal behaviors of small insects that vector [...] Read more.

Some basic aspects of plant disease epidemiology remain largely unknown due to a lack of empirical study methods to experimentally manipulate the position of infections within a single plant or within a plant canopy and the dispersal behaviors of small insects that vector important plant diseases, for example. We present two methods using UV fluorescent particles that, when mixed in a 10% ethanol solution, can be used to create surrogate fungal infections on plant leaves and to field mark whiteflies in situ. When we used a custom-made experimental chamber to measure the velocity of falling particles, we found that the UV fluorescent particles had settlement velocities that overlapped with known fungal plant pathogen spores. In a separate experiment, field applied marks to whiteflies, Bemisia tabaci, were used to estimate straight-line insect vector displacement from source plants as a simple dispersal gradient over a limited distance in a 48 h period. The UV fluorescent particles and airbrushes were relatively inexpensive (USD < 100 total), easily sourced, and usable in a field setting. We believe that the approaches and methods shared in this manuscript can be used to design specific experiments that will fill important plant epidemiological knowledge gaps in future studies. Full article

(This article belongs to the Special Issue Tools and Techniques for Monitoring Pests and Diseases in Agro-Ecosystem)

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15 pages, 4824 KiB

Open AccessArticle

Development and Evaluation of a Loop-Mediated Isothermal Amplifcation (LAMP) Assay for Specific and Sensitive Detection of Puccinia melanocephala Causing Brown Rust in Sugarcane

by Weihuai Wu, Guihua Wang, Han Wang, Liqian Zhu, Yanqiong Liang, Thomas Gbokie, Jr., Ying Lu, Xing Huang, Chunping He, Jianfeng Qin and Kexian Yi

Agronomy 2024, 14(6), 1096; https://doi.org/10.3390/agronomy14061096 - 22 May 2024

Cited by 1 | Viewed by 1288

Abstract

Sugarcane brown rust (SCBR), caused by Puccinia melanocephala, is a destructive fungal disease that has extensively spread in the sugarcane-cultivating regions across the world. Early monitoring plays an important role in predicting the P. melanocephala epidemic and managing SCBR. However, accurately identifying SCBR based [...] Read more.

Sugarcane brown rust (SCBR), caused by Puccinia melanocephala, is a destructive fungal disease that has extensively spread in the sugarcane-cultivating regions across the world. Early monitoring plays an important role in predicting the P. melanocephala epidemic and managing SCBR. However, accurately identifying SCBR based on symptoms and urediniospore morphology at the initial stage is a challenge. Further, it is tedious, time-consuming, labor-intensive, and requires expensive equipment to detect P. melanocephala using PCR-based methods. Loop-mediated isothermal amplification (LAMP) technology is renowned for its speed, simplicity, and low equipment requirements for specifically and sensitively identifying many pathogens. Therefore, in this study, a novel and highly sensitive LAMP assay was developed for the specific detection of P. melanocephala in sugarcane. Here, the internal transcribed spacer (ITS) sequence of P. melanocephala was selected as the target gene for LAMP primer design. Based on the color change of SYBR Green I and gel electrophoresis, specific LAMP primers were screened. Further, the optimal reaction conditions for the LAMP assay were determined at 63 °C for 60 min. The LAMP assay showed a high degree of specificity for the detection of P. melanocephala in sugarcane, with no cross-reactivity with other fungal pathogens. The established LAMP protocol was highly sensitive and can be used to detect as low as 1 pg/μL of P. melanocephala plasmid DNA, which is comparable to that of nested PCR and ~100 times more sensitive than conventional PCR. Finally, the detection rate of the LAMP method was higher than that of conventional and nested PCR in field samples. Full article

(This article belongs to the Special Issue Tools and Techniques for Monitoring Pests and Diseases in Agro-Ecosystem)

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16 pages, 40315 KiB

Open AccessArticle

Rapid Automatic Cacao Pod Borer Detection Using Edge Computing on Low-End Mobile Devices

by Eros Allan Somo Hacinas, Lorenzo Sangco Querol, Kris Lord T. Santos, Evian Bless Matira, Rhodina C. Castillo, Mercedes Arcelo, Divina Amalin and Dan Jeric Arcega Rustia

Agronomy 2024, 14(3), 502; https://doi.org/10.3390/agronomy14030502 - 29 Feb 2024

Cited by 2 | Viewed by 3027

Abstract

The cacao pod borer (CPB) (Conopomorpha cramerella) is an invasive insect that causes significant economic loss for cacao farmers. One of the most efficient ways to reduce CPB damage is to continuously monitor its presence. Currently, most automated technologies for continuous [...] Read more.

The cacao pod borer (CPB) (Conopomorpha cramerella) is an invasive insect that causes significant economic loss for cacao farmers. One of the most efficient ways to reduce CPB damage is to continuously monitor its presence. Currently, most automated technologies for continuous insect pest monitoring rely on an internet connection and a power source. However, most cacao plantations are remotely located and have limited access to internet and power sources; therefore, a simpler and readily available tool is necessary to enable continuous monitoring. This research proposes a mobile application developed for rapid and on-site counting of CPBs on sticky paper traps. A CPB counting algorithm was developed and optimized to enable on-device computations despite memory constraints and limited capacity of low-end mobile phones. The proposed algorithm has an F₁-score of 0.88, with no significant difference from expert counts (R² = 0.97, p-value = 0.55,

α

= 0.05). The mobile application can be used to provide the required information for pest control methods on-demand and is also accessible for low-income farms. This is one of the first few works on enabling on-device processing for insect pest monitoring. Full article

(This article belongs to the Special Issue Tools and Techniques for Monitoring Pests and Diseases in Agro-Ecosystem)

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13 pages, 5116 KiB

Open AccessEditor’s ChoiceArticle

Classification of Plant Leaf Disease Recognition Based on Self-Supervised Learning

by Yuzhi Wang, Yunzhen Yin, Yaoyu Li, Tengteng Qu, Zhaodong Guo, Mingkang Peng, Shujie Jia, Qiang Wang, Wuping Zhang and Fuzhong Li

Agronomy 2024, 14(3), 500; https://doi.org/10.3390/agronomy14030500 - 28 Feb 2024

Cited by 8 | Viewed by 2872

Abstract

Accurate identification of plant diseases is a critical task in agricultural production. The existing deep learning crop disease recognition methods require a large number of labeled images for training, limiting the implementation of large-scale detection. To overcome this limitation, this study explores the [...] Read more.

Accurate identification of plant diseases is a critical task in agricultural production. The existing deep learning crop disease recognition methods require a large number of labeled images for training, limiting the implementation of large-scale detection. To overcome this limitation, this study explores the application of self-supervised learning (SSL) in plant disease recognition. We propose a new model that combines a masked autoencoder (MAE) and a convolutional block attention module (CBAM) to alleviate the harsh requirements of large amounts of labeled data. The performance of the model was validated on the CCMT dataset and our collected dataset. The results show that the improved model achieves an accuracy of 95.35% and 99.61%, recall of 96.2% and 98.51%, and F1 values of 95.52% and 98.62% on the CCMT dataset and our collected dataset, respectively. Compared with ResNet50, ViT, and MAE, the accuracies on the CCMT dataset improved by 1.2%, 0.7%, and 0.8%, respectively, and the accuracy of our collected dataset improved by 1.3%, 1.6%, and 0.6%, respectively. Through experiments on 21 leaf diseases (early blight, late blight, leaf blight, leaf spot, etc.) of five crops, namely, potato, maize, tomato, cashew, and cassava, our model achieved accurate and rapid detection of plant disease categories. This study provides a reference for research work and engineering applications in crop disease detection. Full article

(This article belongs to the Special Issue Tools and Techniques for Monitoring Pests and Diseases in Agro-Ecosystem)

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18 pages, 3568 KiB

Open AccessArticle

Drought Stress Affects Spectral Separation of Maize Infested by Western Corn Rootworm

by Raquel Peron-Danaher, Lorenzo Cotrozzi, Ali Masjedi, Laramy S. Enders, Christian H. Krupke, Michael V. Mickelbart and John J. Couture

Agronomy 2023, 13(10), 2562; https://doi.org/10.3390/agronomy13102562 - 5 Oct 2023

Cited by 2 | Viewed by 1663

Abstract

Root-feeding herbivores present challenges for insect scouting due to the reliance on aboveground visual cues. These challenges intensify in multi-stress environments, where one stressor can mask another. Pre-visual identification of plant stress offers promise in addressing this issue. Hyperspectral data have emerged as [...] Read more.

Root-feeding herbivores present challenges for insect scouting due to the reliance on aboveground visual cues. These challenges intensify in multi-stress environments, where one stressor can mask another. Pre-visual identification of plant stress offers promise in addressing this issue. Hyperspectral data have emerged as a measurement able to identify plant stress before visible symptoms appear. The effectiveness of spectral data to identify belowground stressors using aboveground vegetative measurements, however, remains poorly understood, particularly in multi-stress environments. We investigated the potential of hyperspectral data to detect Western corn rootworm (WCR; Diabrotica virgifera virgirefa) infestations in resistant and susceptible maize genotypes in the presence and absence of drought. Under well-watered conditions, the spectral profiles separated between WCR treatments, but the presence of drought eliminated spectral separation. The foliar spectral profiles separated under drought conditions, irrespective of WCR presence. Spectral data did not classify WCR well; drought was well classified, and the presence of drought further reduced WCR classification accuracy. We found that multiple plant traits were not affected by WCR but were negatively affected by drought. Our study highlights the possibility of detecting WCR and drought stress in maize using hyperspectral data but highlights limitations of the approach for assessing plant health in multi-stress conditions. Full article

(This article belongs to the Special Issue Tools and Techniques for Monitoring Pests and Diseases in Agro-Ecosystem)

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23 pages, 5810 KiB

Open AccessFeature PaperArticle

A Lightweight Crop Pest Detection Algorithm Based on Improved Yolov5s

by Jing Zhang, Jun Wang and Maocheng Zhao

Agronomy 2023, 13(7), 1779; https://doi.org/10.3390/agronomy13071779 - 30 Jun 2023

Cited by 10 | Viewed by 2811

Abstract

The real-time target detection of crop pests can help detect and control pests in time. In this study, we built a lightweight agricultural pest identification method based on modified Yolov5s and reconstructed the original backbone network in tandem with MobileNetV3 to considerably reduce [...] Read more.

The real-time target detection of crop pests can help detect and control pests in time. In this study, we built a lightweight agricultural pest identification method based on modified Yolov5s and reconstructed the original backbone network in tandem with MobileNetV3 to considerably reduce the number of parameters in the network model. At the same time, the ECA attention mechanism was introduced into the MobileNetV3 shallow network to meet the aim of effectively enhancing the network’s performance by introducing a limited number of parameters. A weighted bidirectional feature pyramid network (BiFPN) was utilized to replace the path aggregation network (PAnet) in the neck network to boost the feature extraction of tiny targets. The SIoU loss function was utilized to replace the CIoU loss function to increase the convergence speed and accuracy of the model prediction frame. The updated model was designated ECMB-Yolov5. In this study, we conducted experiments on eight types of common pest dataset photos, and comparative experiments were conducted using common target identification methods. The final model was implemented on an embedded device, the Jetson Nano, for real-time detection, which gave a reference for further application to UAV or unmanned cart real-time detection systems. The experimental results indicated that ECMB-Yolov5 decreased the number of parameters by 80.3% and mAP by 0.8% compared to the Yolov5s model. The real-time detection speed deployed on embedded devices reached 15.2 FPS, which was 5.7 FPS higher than the original model. mAP was improved by 7.1%, 7.3%, 9.9%, and 8.4% for ECMB-Yolov5 compared to Faster R-CNN, Yolov3, Yolov4, and Yolov4-tiny models, respectively. It was verified through experiments that the improved lightweight method in this study had a high detection accuracy while significantly reducing the number of parameters and accomplishing real-time detection. Full article

(This article belongs to the Special Issue Tools and Techniques for Monitoring Pests and Diseases in Agro-Ecosystem)

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