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Advanced Computational Techniques for Plant Disease Detection
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Advanced Computational Techniques for Plant Disease Detection

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 4563

Special Issue Editors

Dr. Haixi Zhang

E-Mail Website
Guest Editor
College of Information Engineering, Northwest A&F University, Xianyang 712100, China
Interests: deep learning; computer vision; large scale image classification; plant identification
Special Issues, Collections and Topics in MDPI journals
Dr. Zhaoqiang Xia

E-Mail Website
Guest Editor
School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710060, China
Interests: visual computing; image search; image recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the realm of agriculture, the timely detection of plant diseases is crucial for ensuring crop health and productivity. With the advent of digital technology, advanced computational techniques have emerged as powerful tools in this endeavor. These techniques leverage cutting-edge algorithms and machine learning models that can be used to analyze vast amounts of data, from high-resolution images to environmental sensors, as well as identify patterns and anomalies indicative of disease. The integration of these advanced computational techniques with traditional agricultural practices has led to smarter, more efficient, and sustainable farming practices. As research continues to advance, the potential for the early detection, prevention, and management of plant diseases will only grow, ultimately benefiting global food supply and the environment.

Dr. Haixi Zhang
Dr. Zhaoqiang Xia
Guest Editors

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Keywords

  • plant disease detection
  • machine learning
  • computer vision

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Published Papers (3 papers)

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Research

22 pages, 2551 KiB  
Article
Remote Sensor Images and Vegetation Indices to Optimize Rice Yield Analysis for Specific Growth Stages Within Extensive Data
by David Fita, Constanza Rubio, Antonio Uris, Sergio Castiñeira-Ibáñez, Belén Franch, Daniel Tarrazó-Serrano and Alberto San Bautista
Appl. Sci. 2025, 15(7), 3870; https://doi.org/10.3390/app15073870 - 1 Apr 2025
Viewed by 395
Abstract
The crop yield in commercial fields is a very important parameter for farmers. The use of Precision Agriculture tools has been shown to improve rice crop yields. One of these tools is remote sensing on satellite platforms. Sentinel-2 provides free data on reflectance [...] Read more.
The crop yield in commercial fields is a very important parameter for farmers. The use of Precision Agriculture tools has been shown to improve rice crop yields. One of these tools is remote sensing on satellite platforms. Sentinel-2 provides free data on reflectance at different wavelengths. Focusing on commercial farms, correlations between the yield and satellite reflectance were studied over several years and locations for ‘JSendra’ rice crops. Four years of yield maps for 706 ha composed the database. Mid tillering-MT, panicle initiation-PI and grain filling-GF reflectance values and Vegetation Indices (VIs) were used. At MT, correlations with the yield were variable (0.23–0.70). At PI, correlations with the yield increased in NIR (0.39–0.85), but the other regions and VIs experienced a decrease. Visible bands and B05 Red Edge were significantly correlated with each other; similarly, B08 NIR was highly correlated with B06, B07, and B8A; SWIR bands were correlated with each other but not with the yield. At GF, the previous pattern was similar. Substantial limitations in estimating yield variability directly from reflectance or VIs were discussed. Two periods were established. The first is designing strategies to increase NIR and decrease red reflectance from MT to PI. The second is avoiding the relationship between crop greenness and NIR from PI to harvest. NIR was a better variable than VIs, but the single use of this band is challenging. Future recommendations focus on the visible–NIR collinearities to interpret differences between years or locations. Full article
(This article belongs to the Special Issue Advanced Computational Techniques for Plant Disease Detection)
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22 pages, 16639 KiB  
Article
Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
by Hengzhao Li, Bowen Tan, Leiming Sun, Hanye Liu, Haixi Zhang and Bin Liu
Appl. Sci. 2024, 14(17), 7695; https://doi.org/10.3390/app14177695 - 31 Aug 2024
Cited by 1 | Viewed by 1256
Abstract
Efficient diagnosis of apple diseases and pests is crucial to the healthy development of the apple industry. However, the existing single-source image-based classification methods have limitations due to the constraints of single-source input image information, resulting in low classification accuracy and poor stability. [...] Read more.
Efficient diagnosis of apple diseases and pests is crucial to the healthy development of the apple industry. However, the existing single-source image-based classification methods have limitations due to the constraints of single-source input image information, resulting in low classification accuracy and poor stability. Therefore, a classification method for apple disease and pest areas based on multi-source image fusion is proposed in this paper. Firstly, RGB images and multispectral images are obtained using drones to construct an apple diseases and pests canopy multi-source image dataset. Secondly, a vegetation index selection method based on saliency attention is proposed, which uses a multi-label ReliefF feature selection algorithm to obtain the importance scores of vegetation indices, enabling the automatic selection of vegetation indices. Finally, an apple disease and pest area multi-label classification model named AMMFNet is constructed, which effectively combines the advantages of RGB and multispectral multi-source images, performs data-level fusion of multi-source image data, and combines channel attention mechanisms to exploit the complementary aspects between multi-source data. The experimental results demonstrated that the proposed AMMFNet achieves a significant subset accuracy of 92.92%, a sample accuracy of 85.43%, and an F1 value of 86.21% on the apple disease and pest multi-source image dataset, representing improvements of 8.93% and 10.9% compared to prediction methods using only RGB or multispectral images. The experimental results also proved that the proposed method can provide technical support for the coarse-grained positioning of diseases and pests in apple orchards and has good application potential in the apple planting industry. Full article
(This article belongs to the Special Issue Advanced Computational Techniques for Plant Disease Detection)
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24 pages, 15389 KiB  
Article
COTTON-YOLO: Enhancing Cotton Boll Detection and Counting in Complex Environmental Conditions Using an Advanced YOLO Model
by Ziao Lu, Bo Han, Luan Dong and Jingjing Zhang
Appl. Sci. 2024, 14(15), 6650; https://doi.org/10.3390/app14156650 - 30 Jul 2024
Cited by 1 | Viewed by 2046
Abstract
This study aims to enhance the detection accuracy and efficiency of cotton bolls in complex natural environments. Addressing the limitations of traditional methods, we developed an automated detection system based on computer vision, designed to optimize performance under variable lighting and weather conditions. [...] Read more.
This study aims to enhance the detection accuracy and efficiency of cotton bolls in complex natural environments. Addressing the limitations of traditional methods, we developed an automated detection system based on computer vision, designed to optimize performance under variable lighting and weather conditions. We introduced COTTON-YOLO, an improved model based on YOLOv8n, incorporating specific algorithmic optimizations and data augmentation techniques. Key innovations include the C2F-CBAM module to boost feature recognition capabilities, the Gold-YOLO neck structure for enhanced information flow and feature integration, and the WIoU loss function to improve bounding box precision. These advancements significantly enhance the model’s environmental adaptability and detection precision. Comparative experiments with the baseline YOLOv8 model demonstrated substantial performance improvements with COTTON-YOLO, particularly a 10.3% increase in the AP50 metric, validating its superiority in accuracy. Additionally, COTTON-YOLO showed efficient real-time processing capabilities and a low false detection rate in field tests. The model’s performance in static and dynamic counting scenarios was assessed, showing high accuracy in static cotton boll counting and effective tracking of cotton bolls in video sequences using the ByteTrack algorithm, maintaining low false detections and ID switch rates even in complex backgrounds. Full article
(This article belongs to the Special Issue Advanced Computational Techniques for Plant Disease Detection)
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