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Agriculture
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Optics and Image Analysis in Modern Agriculture: Transforming Practices and...
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Optics and Image Analysis in Modern Agriculture: Transforming Practices and Unveiling Opportunities

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Technology".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2819

Special Issue Editor

Prof. Dr. Roberto Alves Braga Júnior

E-Mail Website
Guest Editor
Department of Automatica, School of Engineering, Federal University of Lavras—UFLA, Lavras, MG, Brazil
Interests: optical metrology; laser interferometry; dynamic laser speckle
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Optics and image analysis technologies support modern agriculture, providing detailed insights into crop health, soil conditions, and environmental factors at both macro- and micro-scales. These advancements enable farmers to make data-driven decisions, optimize resource usage, and enhance overall productivity. Furthermore, they present opportunities for industries and research centers to develop innovative equipment and protocols. In addition, the ongoing digital revolution continues to drive novelties in optics and image analysis, regularly introducing novel approaches.

Advanced techniques in this domain include remote sensing, multispectral and hyperspectral imaging, 3D reconstruction, digital image processing and analysis, and laser interferometric methods. Additional methods such as near-infrared (NIR) and fluorescence spectroscopy, lidar (light detection and ranging), machine vision, and thermal imaging, in some cases, integrated with deep learning and artificial intelligence (AI) further expand the possibilities for addressing the challenges faced in agriculture. These cutting-edge methods enable the precise monitoring, analysis, and management of agricultural processes, promoting efficiency, sustainability, and innovation in farming practices.

This special issue will highlight the diverse applications of optics and image analysis in agriculture. We invite contributions across a wide range of article types, including original research, reviews, and opinion pieces. Together, we can showcase the transformative potential of these technologies for advancing modern agricultural practices.

Prof. Dr. Roberto Alves Braga Júnior
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • near-infrared (NIR)
  • fluorescence spectroscopy
  • lidar (light detection and ranging)
  • machine vision
  • thermal imaging
  • remote sensing
  • multispectral and hyperspectral imaging
  • 3D reconstruction
  • digital image processing and analysis
  • laser interferometric methods

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Published Papers (5 papers)

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Research

18 pages, 3577 KB  
Article
WT-ResNet: A Non-Destructive Method for Determining the Nitrogen, Phosphorus, and Potassium Content of Sugarcane Leaves Based on Leaf Image
by Cuimin Sun, Junyang Dou, Biao He, Yuxiang Cai and Chengwu Zou
Agriculture 2025, 15(16), 1752; https://doi.org/10.3390/agriculture15161752 - 15 Aug 2025
Viewed by 355
Abstract
Traditional nutritional diagnosis suffers from inefficiency, high cost, and damage when predicting the nitrogen, phosphorus, and potassium content of sugarcane leaves. Non-destructive nutritional diagnosis of sugarcane leaves based on traditional machine learning and deep learning suffers from poor generalization and lower accuracy. To [...] Read more.
Traditional nutritional diagnosis suffers from inefficiency, high cost, and damage when predicting the nitrogen, phosphorus, and potassium content of sugarcane leaves. Non-destructive nutritional diagnosis of sugarcane leaves based on traditional machine learning and deep learning suffers from poor generalization and lower accuracy. To address these issues, this study proposes a novel convolutional neural network called WT-ResNet. This model incorporates wavelet transform into the residual network structure, enabling effective feature extraction from sugarcane leaf images and facilitating the regression prediction of nitrogen, phosphorus, and potassium content in the leaves. By employing a cascade of decomposition and reconstruction, the wavelet transform extracts multi-scale features, which allows for the capture of different frequency components in images. Through the use of shortcut connections, residual structures facilitate the learning of identity mappings within the model. The results show that by analyzing sugarcane leaf images, our model achieves R2 values of 0.9420 for nitrogen content prediction, 0.9084 for phosphorus content prediction, and 0.8235 for potassium content prediction. The accuracy rate for nitrogen prediction reaches 88.24% within a 0.5 tolerance, 58.82% for phosphorus prediction within a 0.1 tolerance, and 70.59% for potassium prediction within a 0.5 tolerance. Compared to other algorithms, WT-ResNet demonstrates higher accuracy. This study aims to provide algorithms for non-destructive sugarcane nutritional diagnosis and technical support for precise sugarcane fertilization. Full article
(This article belongs to the Special Issue Optics and Image Analysis in Modern Agriculture: Transforming Practices and Unveiling Opportunities)
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17 pages, 2649 KB  
Article
Four-Dimensional Hyperspectral Imaging for Fruit and Vegetable Grading
by Laraib Haider Naqvi, Badrinath Balasubramaniam, Jiaqiong Li, Lingling Liu and Beiwen Li
Agriculture 2025, 15(15), 1702; https://doi.org/10.3390/agriculture15151702 - 6 Aug 2025
Viewed by 478
Abstract
Reliable, non-destructive grading of fresh fruit requires simultaneous assessment of external morphology and hidden internal defects. Camera-based grading of fresh fruit using colorimetric (RGB) and near-infrared (NIR) imaging often misses subsurface bruising and cannot capture the fruit’s true shape, leading to inconsistent quality [...] Read more.
Reliable, non-destructive grading of fresh fruit requires simultaneous assessment of external morphology and hidden internal defects. Camera-based grading of fresh fruit using colorimetric (RGB) and near-infrared (NIR) imaging often misses subsurface bruising and cannot capture the fruit’s true shape, leading to inconsistent quality assessment and increased waste. To address this, we developed a 4D-grading pipeline that fuses visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging with structured-light 3D scanning to non-destructively evaluate both internal defects and external form. Our contributions are (1) flagging the defects in fruits based on the reflectance information, (2) accurate shape and defect measurement based on the 3D data of fruits, and (3) an interpretable, decision-tree framework that assigns USDA-style quality (Premium, Grade 1/2, Reject) and size (Small–Extra Large) labels. We demonstrate this approach through preliminary results, suggesting that 4D hyperspectral imaging may offer advantages over single-modality methods by providing clear, interpretable decision rules and the potential for adaptation to other produce types. Full article
(This article belongs to the Special Issue Optics and Image Analysis in Modern Agriculture: Transforming Practices and Unveiling Opportunities)
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14 pages, 10677 KB  
Article
A Seed Vigor Test Through a Biospeckle Laser: A Comparison of Local and Global Analyses
by Bruno Vicentini, Roberto Alves Braga, José Luís Contado, José Eduardo da Silva Gomes and Rolando de Jesus Gonzalez-Peña
Agriculture 2025, 15(14), 1553; https://doi.org/10.3390/agriculture15141553 - 19 Jul 2025
Viewed by 399
Abstract
Seed vigor testing traditionally requires large sample sizes and extended durations. The biospeckle laser (BSL) technique offers a faster, image-based alternative for seed analysis though the standardization of set protocols. This study evaluated the efficiency of local and global BSL analyses in bean [...] Read more.
Seed vigor testing traditionally requires large sample sizes and extended durations. The biospeckle laser (BSL) technique offers a faster, image-based alternative for seed analysis though the standardization of set protocols. This study evaluated the efficiency of local and global BSL analyses in bean seeds (Phaseolus vulgaris L.). Two groups of seeds (872 in total) were classified into high- and low-vigor seeds using the emergence test over 800 samples. The BSL test was then applied to 72 seeds (36 per group), analyzing biological activity locally (vascular and embryonic areas) and globally (whole image). BSL analysis detected significant differences between the groups (p < 0.05). Among the methods, the local analysis of the embryonic axis was most effective (F = 44.252, p = 0.000), showing a clearer distinction than the global analysis (F = 19.484, p = 0.000). The vascular area analysis did not yield significant results. These findings highlight the efficiency of the local BSL analysis at the embryonic axis for vigor tests compared to the global analysis. However, it was observed that the selected point in the local analysis affects the reliability of the vigor test. It was a relevant step toward standardization demanding additional tests in other species and varieties. Full article
(This article belongs to the Special Issue Optics and Image Analysis in Modern Agriculture: Transforming Practices and Unveiling Opportunities)
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17 pages, 3461 KB  
Article
Application of Hyperspectral Imaging for Identification of Melon Seed Variety Using Deep Learning
by Zhiqi Hong, Chu Zhang, Wenjian Song, Xiangbo Nie, Hongxia Ye and Yong He
Agriculture 2025, 15(11), 1139; https://doi.org/10.3390/agriculture15111139 - 25 May 2025
Viewed by 669
Abstract
The accurate identification of melon seed varieties is essential for improving seed purity and the overall quality of melon production. In this study, hyperspectral imaging was used to identify six varieties of melon seeds. Both hyperspectral images and RGB images were generated during [...] Read more.
The accurate identification of melon seed varieties is essential for improving seed purity and the overall quality of melon production. In this study, hyperspectral imaging was used to identify six varieties of melon seeds. Both hyperspectral images and RGB images were generated during hyperspectral image acquisition. The spectral features of seeds were extracted from the hyperspectral images. The image features of the corresponding seeds were manually extracted from the RGB images. Five different datasets were formed using the spectral features and RGB images of the seeds, including seed spectral features, manually extracted seed image features, seed images, the fusion of seed spectral features with manually extracted features, and the fusion of seed spectral features with seed images. Logistic Regression (LR), Support Vector Classification (SVC), and Extreme Gradient Boosting (XGBoost) were used to establish classification models using spectral features and the manually extracted image features. Convolutional Neural Network (CNN) models were established using the five datasets. The results indicated that the CNN models achieved good performance in all five datasets, with classification accuracies exceeding 90% for the training, validation, and test sets. Also, CNN using the fused datasets obtained optimal performance, achieving classification accuracies exceeding 97% for the training, validation, and test sets. The results indicated that both spectral features and image features can be used to identify the six varieties of melon seeds, and their fusion of spectral features and image features can improve classification performance. These findings provide an alternative approach for melon seed variety identification, which can also be extended to other seed types. Full article
(This article belongs to the Special Issue Optics and Image Analysis in Modern Agriculture: Transforming Practices and Unveiling Opportunities)
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20 pages, 15232 KB  
Article
Swift Transfer of Lactating Piglet Detection Model Using Semi-Automatic Annotation Under an Unfamiliar Pig Farming Environment
by Qi’an Ding, Fang Zheng, Luo Liu, Peng Li and Mingxia Shen
Agriculture 2025, 15(7), 696; https://doi.org/10.3390/agriculture15070696 - 25 Mar 2025
Cited by 1 | Viewed by 413
Abstract
Manual annotation of piglet imagery across varied farming environments is labor-intensive. To address this, we propose a semi-automatic approach within an active learning framework that integrates a pre-annotation model for piglet detection. We further examine how data sample composition influences pre-annotation efficiency to [...] Read more.
Manual annotation of piglet imagery across varied farming environments is labor-intensive. To address this, we propose a semi-automatic approach within an active learning framework that integrates a pre-annotation model for piglet detection. We further examine how data sample composition influences pre-annotation efficiency to enhance the deployment of lactating piglet detection models. Our study utilizes original samples from pig farms in Jingjiang, Suqian, and Sheyang, along with new data from the Yinguang pig farm in Danyang. Using the YOLOv5 framework, we constructed both single and mixed training sets of piglet images, evaluated their performance, and selected the optimal pre-annotation model. This model generated bounding box coordinates on processed new samples, which were subsequently manually refined to train the final model. Results indicate that expanding the dataset and diversifying pigpen scenes significantly improve pre-annotation performance. The best model achieved a test precision of 0.921 on new samples, and after manual calibration, the final model exhibited a training precision of 0.968, a recall of 0.952, and an average precision of 0.979 at the IoU threshold of 0.5. The model demonstrated robust detection under various lighting conditions, with bounding boxes closely conforming to piglet contours, thereby substantially reducing manual labor. This approach is cost-effective for piglet segmentation tasks and offers strong support for advancing smart agricultural technologies. Full article
(This article belongs to the Special Issue Optics and Image Analysis in Modern Agriculture: Transforming Practices and Unveiling Opportunities)
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