AgriEngineering

Research

33 pages, 4952 KB

Open AccessArticle

Modified RefineNet with Attention-Based Fusion for Multi-Class Classification of Corn and Pepper Plant Diseases

by Maramreddy Srinivasulu and Sandipan Maiti

AgriEngineering 2026, 8(4), 122; https://doi.org/10.3390/agriengineering8040122 - 30 Mar 2026

Viewed by 239

Early and precise detection of plant diseases is essential for safeguarding crop yield and ensuring sustainable agricultural practices. In this study, we propose the Modified RefineNet with Attention based Fusion (MoRefNet-AF), a Modified RefineNet architecture enhanced with attention-based fusion for multi-class classification of [...] Read more.

Early and precise detection of plant diseases is essential for safeguarding crop yield and ensuring sustainable agricultural practices. In this study, we propose the Modified RefineNet with Attention based Fusion (MoRefNet-AF), a Modified RefineNet architecture enhanced with attention-based fusion for multi-class classification of corn (maize) and Pepper leaf diseases. Unlike the original RefineNet, which was segmentation-oriented and computationally heavy, MoRefNet-AF is redesigned for lightweight and discriminative classification. The modifications include replacing standard convolutions with depthwise separable convolutions for efficiency, adopting the Mish activation function for smoother gradient flow, redesigning the multi-resolution fusion module with concatenation and shared convolution for richer cross-scale integration, and incorporating Squeeze-and-Excitation (SE) blocks for adaptive channel recalibration. Additionally, Chained Residual Pooling (CRP) with atrous convolutions enhances contextual representation, while global average pooling with dense layers improves classification readiness. When evaluated on a curated six-class dataset combining PlantVillage and Mendeley leaf disease repositories, MoRefNet-AF achieved 99.88% accuracy, 99.74% precision, 99.73% recall, 99.95% F1-score, and 99.73% specificity. These results outperform strong baselines including ResNet152V2, DenseNet201, EfficientNet-B0, and ConvNeXt-Tiny, while maintaining only 0.3 M parameters. With its compact design and TensorFlow Lite (v2.13) compatibility, MoRefNet-AF offers a robust, lightweight, and real-time deployable solution for precision agriculture and smart plant disease monitoring. Full article

(This article belongs to the Special Issue The Application of Machine Learning and Deep Learning Techniques in Agriculture)

► Show Figures

Figure 1

21 pages, 3595 KB

Open AccessArticle

Machine Learning Predicts Drivers of Biochar-Diazotrophic Bacteria in Enhancing Brachiaria Growth and Soil Quality

by Thallyta das Graças Espíndola da Silva, Diogo Paes da Costa, Rafaela Félix da França, Argemiro Pereira Martins Filho, Maria Renaí Ferreira Barbosa, Jamilly Alves de Barros, Gustavo Pereira Duda, Claude Hammecker, José Romualdo de Sousa Lima, Ademir Sérgio Ferreira de Araújo and Erika Valente de Medeiros

AgriEngineering 2026, 8(3), 118; https://doi.org/10.3390/agriengineering8030118 - 20 Mar 2026

Viewed by 476

Abstract

Data-driven approaches are increasingly required to optimize biofertilization strategies in forage systems. Machine learning (ML) provides an efficient tool for identifying functional drivers in complex plant–soil–microbe systems, offering important perspectives for precision data-driven agriculture. However, despite its potential, ML remains data-driven in studies [...] Read more.

Data-driven approaches are increasingly required to optimize biofertilization strategies in forage systems. Machine learning (ML) provides an efficient tool for identifying functional drivers in complex plant–soil–microbe systems, offering important perspectives for precision data-driven agriculture. However, despite its potential, ML remains data-driven in studies involving diazotrophic inoculation using biochar as a pelletizing material, particularly in forage grasses. This study applied ML to predict the key drivers controlling Brachiaria brizantha performance and soil quality under biochar-pelletized diazotrophic bacteria (DB). Five isolates were inoculated with or without biochar, and plant traits and soil attributes, including pH, potassium, phosphorus, sodium, and urease activity were evaluated. These data were integrated into multivariate analyses and ML algorithms, including Linear Discriminant Analysis, Random Forest, and Support Vector Machine, to identify the functional drivers that best discriminate treatment performance and uncover mechanistic functional drivers. All isolates increased soil potassium content, with the highest values in the biochar amended treatments, and a 39% increase. Soil pH and urease activity were significantly modulated by isolate identity, while biomass allocation patterns differed among treatments. Overall, the results highlight that biochar pelletization can enhance the effectiveness of DB inoculants. ML revealed that dry foliar biomass, soil pH, and fresh root weight were the most predictive variables, highlighting consistent signatures explaining plant–soil responses to biochar-pelletized DB. These findings demonstrate that interpretable ML can disentangle complex plant–soil–microbe interactions, support precision biofertilization design, and serve as an efficient decision-support tool for sustainable pasture management. Beyond the present system, this study establishes a transferable and scalable analytical framework for precision biofertilization strategies in forage systems and other biochar-mediated agroecosystems, advancing predictive and data-driven approaches in sustainable agricultural engineering. Full article

(This article belongs to the Special Issue The Application of Machine Learning and Deep Learning Techniques in Agriculture)

► Show Figures

Figure 1

18 pages, 1263 KB

Open AccessArticle

Comparative Evaluation of Machine Learning Algorithms for the Identification and Morphological Classification of Rice Grains

by Julián Coronel-Reyes, Alexander Haro-Sarango, Carlota Delgado-Vera and Johnny Triviño-Sánchez

AgriEngineering 2026, 8(3), 100; https://doi.org/10.3390/agriengineering8030100 - 6 Mar 2026

Viewed by 485

Abstract

Machine learning has enhanced rice grain classification by enabling accurate, automated, and objective morphological analysis, supporting quality control and varietal selection. This study compared the performance of several algorithms in identifying three Ecuadorian rice varieties (INIAP-11, INIAP-12, and INIAP-20) using a balanced dataset [...] Read more.

Machine learning has enhanced rice grain classification by enabling accurate, automated, and objective morphological analysis, supporting quality control and varietal selection. This study compared the performance of several algorithms in identifying three Ecuadorian rice varieties (INIAP-11, INIAP-12, and INIAP-20) using a balanced dataset of morphological features. Five models were trained with cross-validation and evaluated using multi-class metrics. Significant differences among varieties particularly in area, length, and eccentricity confirmed their discriminative potential. Initially, models were trained using all morphological variables. However, to optimize training time and computational cost, the study also evaluated model performance after applying dimensionality reduction through Principal Component Analysis (PCA). This approach enabled assessing whether reduced feature spaces could maintain competitive predictive performance while improving efficiency. Overall, all algorithms performed well, but only the Artificial Neural Network (ANN) and Support Vector Classifier (SVC) demonstrated strong generalization without overfitting. In contrast, Random Forest achieved perfect accuracy in training but decreased performance in testing. In conclusion, ANN and SVC emerged as the most robust alternatives for rice grain morphological classification, while the PCA results highlight the value of dimensionality reduction as a strategy to enhance computational scalability without substantially compromising accuracy. The objective of the present study is to train, evaluate, and compare different machine learning algorithms for the classification of three types of rice grains, in order to determine the best model for this task based on seven morphological characteristics of the grains applying machine learning algorithms with and without dimensional reduction. Full article

(This article belongs to the Special Issue The Application of Machine Learning and Deep Learning Techniques in Agriculture)

► Show Figures

Figure 1

33 pages, 40829 KB

Open AccessArticle

Lightweight Hybrid Deep Learning for Strawberry Disease Recognition and Edge Deployment Using Dynamic Multi-Scale CNN–Transformer Fusion

by Nasreddine Haqiq, Mounia Zaim, Mohamed Sbihi, Mustapha El Alaoui, Khalid El Amraoui, Youssef El Kazini, Hassane Roukhe and Lhoussaine Masmoudi

AgriEngineering 2026, 8(2), 75; https://doi.org/10.3390/agriengineering8020075 - 22 Feb 2026

Viewed by 602

Abstract

To implement a successful strawberry (Fragaria × ananassa) farming, fungal diseases must be detected in a timely manner so that informed crop protection decisions can be made. While field scouting is an option, it is manual and labor intensive. Scouting is also inaccurate [...] Read more.

To implement a successful strawberry (Fragaria × ananassa) farming, fungal diseases must be detected in a timely manner so that informed crop protection decisions can be made. While field scouting is an option, it is manual and labor intensive. Scouting is also inaccurate and reduces efficiency due to micro-climatic lighting and field clutter, among other factors. StrawberryDualNet is a framework that supports Integrated Pest Management and automates symptom surveillance. We present dual-path CNN–Transformer fusion design that integrates two branches: a dynamic multi-scale convolution and a lightweight transformer. The former is able to capture fine-grained morphological lesion textures, while the latter captures overall contextual patterns. The two representations are fused through a learnable gating mechanism to decrease visual uncertainty amongst differing symptoms. We used a stratified five-fold cross-validation to evaluate the framework among five economically significant pathogens. Our approach significantly outperformed other automated scouting baselines, achieving 95.1% accuracy and 95.3% precision, respectively, and it is successful for Anthracnose, Gray Mold, Powdery Mildew, Rhizopus Rot, and Black Spot. The model is also scaled down compared to others (0.04 M parameters; 0.72 MB, 13–20× smaller than MobileNetV2/ShuffleNetV2) and is thus able to be deployed on devices that are lacking computational resources. For edge feasibility, we assessed reduced-precision inference; 16-bit floating point quantization preserved baseline performance at 83 FPS, whereas 8-bit integer quantization caused notable accuracy degradation. Overall, the proposed local–global fusion design provides an accurate, interpretable, and scalable tool for real-time disease phenotyping in precision horticulture. Full article

(This article belongs to the Special Issue The Application of Machine Learning and Deep Learning Techniques in Agriculture)

► Show Figures

Figure 1

23 pages, 7016 KB

Open AccessArticle

Class Imbalance-Aware Deep Learning Approach for Apple Leaf Disease Recognition

by Emrah Fidan, Serra Aksoy, Pinar Demircioglu and Ismail Bogrekci

AgriEngineering 2026, 8(2), 70; https://doi.org/10.3390/agriengineering8020070 - 16 Feb 2026

Viewed by 487

Abstract

Apple leaf disease identification with high precision is one of the main challenges in precision agriculture. The datasets usually have class imbalance problems and environmental changes, which negatively impact deep learning approaches. In this paper, an ablation study is proposed to test three [...] Read more.

Apple leaf disease identification with high precision is one of the main challenges in precision agriculture. The datasets usually have class imbalance problems and environmental changes, which negatively impact deep learning approaches. In this paper, an ablation study is proposed to test three different scenarios: V1, a hybrid balanced dataset consisting of 10,028 images; V2, an imbalanced dataset as a baseline consisting of 14,582 original images; and V3, a 3× physical augmentation approach based on the 14,582 images. The classification performance of YOLOv11x was benchmarked against three state-of-the-art CNN architectures: ResNet-152, DenseNet-201, and EfficientNet-B1. The methodology incorporates controlled downsampling for dominant classes alongside scenario-based augmentation for minority classes, utilizing CLAHE-based texture enhancement, illumination simulation, and sensor noise generation. All the models were trained for up to 100 epochs under identical experimental conditions, with early stopping based on validation performance and an 80/20 train-validation split. The experimental results demonstrate that the impact of balancing strategies is model-dependent and does not universally improve performance. This highlights the importance of aligning data balancing strategies with architectural characteristics rather than applying uniform resampling approaches. YOLOv11x achieved its peak accuracy of 99.18% within the V3 configuration, marking a +0.62% improvement over the V2 baseline (99.01%). In contrast, EfficientNet-B1 reached its optimal performance in the V2 configuration (98.43%) without additional intervention. While all the models exhibited consistently high AUC values (≥99.94%), DenseNet-201 achieved the highest value (99.97%) across both V2 and V3 configurations. In fine-grained discrimination, the superior performance of YOLOv11x on challenging cases is verified, with only one incorrect classification (Rust to Scab), while ResNet-152 and DenseNet-201 incorrectly classified eight and seven samples, respectively. Degradation sensitivity analysis under controlled Gaussian noise and motion blur indicated that CNN baseline models maintained stable performance. High minority-class reliability, including a 96.20% F1-score for Grey Spot and 100% precision for Mosaic, further demonstrates effective fine-grained discrimination. Results indicate that data preservation with physically inspired augmentation (V3) is better than resampling-based balancing (V1), especially in terms of global accuracy and minority-class performance. Full article

(This article belongs to the Special Issue The Application of Machine Learning and Deep Learning Techniques in Agriculture)

► Show Figures

Figure 1

29 pages, 4179 KB

Open AccessArticle

Ontology-Enhanced Deep Learning for Early Detection of Date Palm Diseases in Smart Farming Systems

by Naglaa E. Ghannam, H. Mancy, Asmaa Mohamed Fathy and Esraa A. Mahareek

AgriEngineering 2026, 8(1), 29; https://doi.org/10.3390/agriengineering8010029 - 13 Jan 2026

Viewed by 845

Abstract

Early and accurate date palm disease detection is the key to successful smart farming ecosystem sustainability. In this paper, we introduce DoST-DPD, a new Dual-Stream Transformer architecture for multimodal disease diagnosis utilizing RGB, thermal and NIR imaging. In contrast with standard deep learning [...] Read more.

Early and accurate date palm disease detection is the key to successful smart farming ecosystem sustainability. In this paper, we introduce DoST-DPD, a new Dual-Stream Transformer architecture for multimodal disease diagnosis utilizing RGB, thermal and NIR imaging. In contrast with standard deep learning approaches, our model receives ontology-based semantic supervision (via per-dataset OWL ontologies), enabling knowledge injection via SPARQL-driven reasoning during training. This structured knowledge layer not only improves multimodal feature correspondence but also restricts label consistency for improving generalization performance, particularly in early disease diagnosis. We tested our proposed method on a comprehensive set of five benchmarks (PlantVillage, PlantDoc, Figshare, Mendeley, and Kaggle Date Palm) together with domain-specific ontologies. An ablation study validates the effectiveness of incorporating ontology supervision, consistently improving the performance across Accuracy, Precision, Recall, F1-Score and AUC. We achieve state-of-the-art performance over five widely recognized baselines (PlantXViT, Multi-ViT, ERCP-Net, andResNet), with our model DoST-DPD achieving the highest Accuracy of 99.3% and AUC of 98.2% on the PlantVillage dataset. In addition, ontology-driven attention maps and semantic consistency contributed to high interpretability and robustness in multiple crop and imaging modalities. Results: This work presents a scalable roadmap for ontology-integrated AI systems in agriculture and illustrates how structured semantic reasoning can directly benefit multimodal plant disease detection systems. The proposed model demonstrates competitive performance across multiple datasets and highlights the unique advantage of integrating ontology-guided supervision in multimodal crop disease detection. Full article

(This article belongs to the Special Issue The Application of Machine Learning and Deep Learning Techniques in Agriculture)

► Show Figures

Figure 1

21 pages, 5143 KB

Open AccessArticle

Comparative Study of the Performance of SqueezeNet and GoogLeNet CNN Models in the Identification of Kazakhstani Potato Varieties

by Zhandos Shynybay, Tsvetelina Georgieva, Eleonora Nedelcheva, Jakhfer Alikhanov, Aidar Moldazhanov, Dmitriy Zinchenko, Maigul Bakytova, Aidana Sapargali and Plamen Daskalov

AgriEngineering 2026, 8(1), 17; https://doi.org/10.3390/agriengineering8010017 - 4 Jan 2026

Viewed by 645

Abstract

Kazakhstan’s growing potato industry underscores the need to develop and apply digital solutions that boost grading efficiency. A comparison between two traditional deep neural network architectures used to classify color images of potatoes from Kazakhstan is discussed in the paper. Ten representative varieties [...] Read more.

Kazakhstan’s growing potato industry underscores the need to develop and apply digital solutions that boost grading efficiency. A comparison between two traditional deep neural network architectures used to classify color images of potatoes from Kazakhstan is discussed in the paper. Ten representative varieties of Kazakhstani potatoes were selected as objects of study: Alians, Alians mini, Astana, Astana mini, Edem, Edem mini, Nerli, Nerli mini, Zhanaisan, and Zhanaisan mini. Two convolutional neural network (CNN) models, SqueezeNet and GoogLeNet, were refined via transfer learning employing three optimization approaches. Then, they were used to classify the potato images. A comparison of the two neural networks’ classification performance was conducted using common evaluation criteria—accuracy, precision, F1 score, and recall—alongside a confusion matrix to highlight misclassified samples. The comparative analysis demonstrated that both CNN architectures—SqueezeNet and GoogLeNet—achieve high classification accuracy for Kazakhstani potato varieties, with the best performance on Astana and Zhanaisan (>97%). The study confirms the applicability of lightweight CNNs for digital varietal identification and automated quality assessment of seed potatoes under controlled imaging conditions. The developed approach is the first comparative CNN-based varietal identification of Kazakhstani potato tubers using transfer learning and contributes to the digitalization of potato breeding, and provides a baseline for future real-time sorting systems using deep learning. Full article

(This article belongs to the Special Issue The Application of Machine Learning and Deep Learning Techniques in Agriculture)

► Show Figures

Figure 1

23 pages, 3864 KB

Open AccessArticle

Lightweight and Accurate Deep Learning for Strawberry Leaf Disease Recognition: An Interpretable Approach

by Raquel Ochoa-Ornelas, Alberto Gudiño-Ochoa, Ansel Y. Rodríguez González, Leonardo Trujillo, Daniel Fajardo-Delgado and Karla Liliana Puga-Nathal

AgriEngineering 2025, 7(10), 355; https://doi.org/10.3390/agriengineering7100355 - 21 Oct 2025

Cited by 4 | Viewed by 2088

Abstract

Background/Objectives: Strawberry crops are vulnerable to fungal diseases that severely affect yield and quality. Deep learning has shown strong potential for plant disease recognition; however, most architectures rely on tens of millions of parameters, limiting their use in low-resource agricultural settings. This [...] Read more.

Background/Objectives: Strawberry crops are vulnerable to fungal diseases that severely affect yield and quality. Deep learning has shown strong potential for plant disease recognition; however, most architectures rely on tens of millions of parameters, limiting their use in low-resource agricultural settings. This study presents Light-MobileBerryNet, a lightweight and interpretable model designed to achieve accurate strawberry disease classification while remaining computationally efficient for potential use on mobile and edge devices. Methods: The model, inspired by MobileNetV3-Small, integrates inverted residual blocks, depthwise separable convolutions, squeeze-and-excitation modules, and Swish activation to enhance efficiency. A publicly available dataset was processed using CLAHE and data augmentation, and split into training, validation, and test subsets under consistent conditions. Performance was benchmarked against state-of-the-art CNNs. Results: Light-MobileBerryNet achieved 96.6% accuracy, precision, recall, and F1-score, with a Matthews correlation coefficient of 0.96, while requiring fewer than one million parameters (~2 MB). Grad-CAM confirmed that predictions focused on biologically relevant lesion regions. Conclusions: Light-MobileBerryNet approaches state-of-the-art performance with a fraction of the computational cost, providing a practical and interpretable solution for precision agriculture. Full article

(This article belongs to the Special Issue The Application of Machine Learning and Deep Learning Techniques in Agriculture)

► Show Figures

Figure 1

30 pages, 4298 KB

Open AccessArticle

Integrating Convolutional, Transformer, and Graph Neural Networks for Precision Agriculture and Food Security

by Esraa A. Mahareek, Mehmet Akif Cifci and Abeer S. Desuky

AgriEngineering 2025, 7(10), 353; https://doi.org/10.3390/agriengineering7100353 - 19 Oct 2025

Cited by 3 | Viewed by 2512

Abstract

Ensuring global food security requires accurate and robust solutions for crop health monitoring, weed detection, and large-scale land-cover classification. To this end, we propose AgroVisionNet, a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) for local feature extraction, Vision Transformers (ViTs) [...] Read more.

Ensuring global food security requires accurate and robust solutions for crop health monitoring, weed detection, and large-scale land-cover classification. To this end, we propose AgroVisionNet, a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) for local feature extraction, Vision Transformers (ViTs) for capturing long-range global dependencies, and Graph Neural Networks (GNNs) for modeling spatial relationships between image regions. The framework was evaluated on five diverse benchmark datasets—PlantVillage (leaf-level disease detection), Agriculture-Vision (field-scale anomaly segmentation), BigEarthNet (satellite-based land-cover classification), UAV Crop and Weed (weed segmentation), and EuroSAT (multi-class land-cover recognition). Across these datasets, AgroVisionNet consistently outperformed strong baselines including ResNet-50, EfficientNet-B0, ViT, and Mask R-CNN. For example, it achieved 97.8% accuracy and 95.6% IoU on PlantVillage, 94.5% accuracy on Agriculture-Vision, 92.3% accuracy on BigEarthNet, 91.5% accuracy on UAV Crop and Weed, and 96.4% accuracy on EuroSAT. These results demonstrate the framework’s robustness across tasks ranging from fine-grained disease detection to large-scale anomaly mapping. The proposed hybrid approach addresses persistent challenges in agricultural imaging, including class imbalance, image quality variability, and the need for multi-scale feature integration. By combining complementary architectural strengths, AgroVisionNet establishes a new benchmark for deep learning applications in precision agriculture. Full article

(This article belongs to the Special Issue The Application of Machine Learning and Deep Learning Techniques in Agriculture)

► Show Figures

Figure 1

22 pages, 3646 KB

Open AccessArticle

Machine Learning in the Classification of RGB Images of Maize (Zea mays L.) Using Texture Attributes and Different Doses of Nitrogen

by Thiago Lima da Silva, Fernanda de Fátima da Silva Devechio, Marcos Silva Tavares, Jamile Raquel Regazzo, Edson José de Souza Sardinha, Liliane Maria Romualdo Altão, Gabriel Pagin, Adriano Rogério Bruno Tech and Murilo Mesquita Baesso

AgriEngineering 2025, 7(10), 317; https://doi.org/10.3390/agriengineering7100317 - 23 Sep 2025

Cited by 1 | Viewed by 1431

Abstract

Nitrogen fertilization is decisive for maize productivity, fertilizer use efficiency, and sustainability, which calls for fast and nondestructive nutritional diagnosis. This study evaluated the classification of maize plant nutritional status from red, green, and blue (RGB) leaf images using texture attributes. A greenhouse [...] Read more.

Nitrogen fertilization is decisive for maize productivity, fertilizer use efficiency, and sustainability, which calls for fast and nondestructive nutritional diagnosis. This study evaluated the classification of maize plant nutritional status from red, green, and blue (RGB) leaf images using texture attributes. A greenhouse experiment was conducted under a completely randomized factorial design with four nitrogen doses, one maize hybrid Pioneer 30F35, and four replicates, at two sampling times corresponding to distinct phenological stages, totaling thirty-two experimental units. Images were processed with the gray-level cooccurrence matrix computed at three distances 1, 3, and 5 pixels and four orientations 0°, 45°, 90°, and 135°, yielding eight texture descriptors that served as inputs to five supervised classifiers: an artificial neural network, a support vector machine, k nearest neighbors, a decision tree, and Naive Bayes. The results indicated that texture descriptors discriminated nitrogen doses with good performance and moderate computational cost, and that homogeneity, dissimilarity, and contrast were the most informative attributes. The artificial neural network showed the most stable performance at both stages, followed by the support vector machine and k nearest neighbors, whereas the decision tree and Naive Bayes were less suitable. Confusion matrices and receiver operating characteristic curves indicated greater separability for omission and excess classes, with D1 standing out, and the patterns were consistent with the chemical analysis. Future work should include field validation, multiple seasons and genotypes, integration with spectral indices and multisensor data, application of model explainability techniques, and assessment of latency and scalability in operational scenarios. Full article

(This article belongs to the Special Issue The Application of Machine Learning and Deep Learning Techniques in Agriculture)

► Show Figures

Figure 1

18 pages, 5730 KB

Open AccessArticle

Automated Physical Feature Extraction of Namdokmai Sithong Mangoes Using YOLOv8 and Image Processing Techniques

by Sujitra Arwatchananukul, Suphapol Wongsawat, Saowapa Chaiwong, Min Chen and Rattapon Saengrayap

AgriEngineering 2025, 7(9), 312; https://doi.org/10.3390/agriengineering7090312 - 22 Sep 2025

Viewed by 1358

Abstract

Accurate and consistent measurements of geometric features such as fruit length and width are essential for the quality assessment of Namdokmai Sithong mangoes. Traditional manual methods are labor-intensive and prone to inconsistency. This study presented an automated system for geometric feature extraction of [...] Read more.

Accurate and consistent measurements of geometric features such as fruit length and width are essential for the quality assessment of Namdokmai Sithong mangoes. Traditional manual methods are labor-intensive and prone to inconsistency. This study presented an automated system for geometric feature extraction of Namdokmai Sithong mangoes using a YOLOv8-based object detection model. The framework automated the process of measuring key morphological traits, specifically fruit length and width, to improve accuracy and consistency in quality assessment. The model identified an anatomically meaningful reference point for guiding axis-based measurements by detecting the mango and its peduncle. HSV-based image segmentation combined with morphological operations and edge detection effectively calculated the major (length) and minor (top and bottom width) axes of the fruit. Evaluation on 30 test images showed that the proposed method achieved error rates below 5% in over 90% of samples, with average deviations for fruit length typically under 1.5%. The system was implemented as a standalone Python (version 3.12.8) application and demonstrated high potential for use in real-time, automated fruit grading scenarios. Full article

(This article belongs to the Special Issue The Application of Machine Learning and Deep Learning Techniques in Agriculture)

► Show Figures

Figure 1

18 pages, 5991 KB

Open AccessArticle

Interpretable Citrus Fruit Quality Assessment Using Vision Transformers and Lightweight Large Language Models

by Zineb Jrondi, Abdellatif Moussaid and Moulay Youssef Hadi

AgriEngineering 2025, 7(9), 286; https://doi.org/10.3390/agriengineering7090286 - 3 Sep 2025

Cited by 3 | Viewed by 2264

Abstract

This study introduces an interpretable deep learning pipeline for citrus fruit quality classification using the ViT-Base model vit_base_patch16_224 and Microsoft’s Phi-3-mini LLM. The ViT model, fine-tuned on resized 224 × 224 images with ImageNet weights, classifies fruits into good, damaged, and rotten categories, [...] Read more.

This study introduces an interpretable deep learning pipeline for citrus fruit quality classification using the ViT-Base model vit_base_patch16_224 and Microsoft’s Phi-3-mini LLM. The ViT model, fine-tuned on resized 224 × 224 images with ImageNet weights, classifies fruits into good, damaged, and rotten categories, achieving 98.29% accuracy. For interpretability, Grad-CAM highlights damaged regions, while the Phi-3-mini generates human-readable diagnostic reports. The system runs efficiently on edge devices, enabling real-time, on-site quality assessment. This approach enhances transparency and decision-making, showing strong potential for deployment in the citrus industry. Full article

(This article belongs to the Special Issue The Application of Machine Learning and Deep Learning Techniques in Agriculture)

► Show Figures

Figure 1

20 pages, 7541 KB

Open AccessArticle

Multi-Species Fruit-Load Estimation Using Deep Learning Models

by Tae-Woong Yoo and Il-Seok Oh

AgriEngineering 2025, 7(7), 220; https://doi.org/10.3390/agriengineering7070220 - 7 Jul 2025

Viewed by 2058

Abstract

Accurate estimation of fruit quantity is essential for efficient harvest management, storage, transportation, and marketing in the agricultural industry. To address the limited generalizability of single-species models, this study presents a comprehensive deep learning-based framework for multi-species fruit-load estimation, leveraging the MetaFruit dataset, [...] Read more.

Accurate estimation of fruit quantity is essential for efficient harvest management, storage, transportation, and marketing in the agricultural industry. To address the limited generalizability of single-species models, this study presents a comprehensive deep learning-based framework for multi-species fruit-load estimation, leveraging the MetaFruit dataset, which contains images of five fruit species collected under diverse orchard conditions. Four representative object detection and regression models—YOLOv8, RT-DETR, Faster R-CNN, and a U-Net-based heatmap regression model—were trained and compared as part of the proposed multi-species learning strategy. The models were evaluated on both the internal MetaFruit dataset and two external datasets, NIHS-JBNU and Peach, to assess their generalization performance. Among them, YOLOv8 and the RGBH heatmap regression model achieved F1-scores of 0.7124 and 0.7015, respectively, on the NIHS-JBNU dataset. These results indicate that a deep learning-based multi-species training strategy can significantly enhance the generalizability of fruit-load estimation across diverse field conditions. Full article

(This article belongs to the Special Issue The Application of Machine Learning and Deep Learning Techniques in Agriculture)

► Show Figures

Figure 1

16 pages, 3375 KB

Open AccessArticle

Tomato Leaf Detection, Segmentation, and Extraction in Real-Time Environment for Accurate Disease Detection

by Shahab Ul Islam, Giampaolo Ferraioli and Vito Pascazio

AgriEngineering 2025, 7(4), 120; https://doi.org/10.3390/agriengineering7040120 - 11 Apr 2025

Cited by 4 | Viewed by 4640

Abstract

Agricultural production is a critical sector that directly impacts the economy and social life of any society. The identification of plant disease in a real-time environment is a significant challenge for agriculture production. For accurate plant disease detection, precise detection of plant leaves [...] Read more.

Agricultural production is a critical sector that directly impacts the economy and social life of any society. The identification of plant disease in a real-time environment is a significant challenge for agriculture production. For accurate plant disease detection, precise detection of plant leaves is a meaningful and challenging task for developing smart agricultural systems. Most researchers train and test models on synthetic images. So, when using that model in a real-time scenario, it does not give a satisfactory result because when a model trained on images of leaves is fed with the image of the plant, then its accuracy is affected. In this research work, we have integrated two models, the Segment Anything Model (SAM) with YOLOv8, to detect the tomato leaf of a tomato plant, mask the leaf, and extract the leaf in a real-time environment. To improve the performance of leaf disease detection in plant leaves in a real-time environment, we need to detect leaves accurately. We developed a system that will detect the leaf, mask the leaf, extract the leaf, and then detect the disease in that specific leaf. For leaf detection, the modified YOLOv8 is used, and for masking and extraction of the leaf images from the tomato plant, the Segment Anything Model (SAM) is used. Then, for that specific leaf, an image is provided to the deep neural network to detect the disease. Full article

(This article belongs to the Special Issue The Application of Machine Learning and Deep Learning Techniques in Agriculture)

► Show Figures

Figure 1

Journal Menu

Journal Browser

The Application of Machine Learning and Deep Learning Techniques in Agriculture

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (14 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI