Deep Learning Techniques for Citrus Disease Detection: A Comprehensive Review

Performance Evaluation of Deep Learning Architectures for Detecting Citrus Crop HLB Abnormalities

Early detection of diseases like Huanglongbing (HLB), also known as citrus greening, is crucial for maintaining citrus crop health and productivity. HLB is a devastating disease that significantly impacts global citrus production, and effective, low-cost detection methods are needed for timely intervention. Convolutional neural networks (CNNs) have emerged as powerful tools for automated disease detection through computer vision, but distinguishing HLB from other citrus disorders remains a challenge. Traditional diagnostic methods, such as quantitative real-time polymerase chain reaction, are expensive and require large datasets for training CNN models. One of the promising solutions to the problem of a lack of labeled images is the utilization of transfer learning and pre-trained CNNs, which can still produce a good result with a smaller dataset. The study aims at comparing the performance of the series (AlexNet, VGG19) and the directed acyclic graph (DAG) CNN architectures, such as ResNet50, and Inception-V3, in three-class classification of Huanglongbing (HLB), healthy leaves, and 10 other citrus abnormalities. To do this, a dataset of 955 color images of Citrus leaves from the southern part of India was chosen, where 10-fold cross-validation was done. The results found that all CNN models could reach the sensitivity of HLB detection above 98%. Among them, VGG19 yielded the highest sensitivity in all experiments, even though it had fewer parameters than Inception-V3. These findings indicate that models with more parameters are capable of dealing with the problems of small datasets, thus allowing transfer learning to be effective for HLB detection. This work reveals the selection of CNN models for the detection of disease in citrus, which is a cost-effective and portable solution that can aid small-scale farmers in low-income areas to lessen crop losses.

Deep Learning for Plant Disease Detection: A Systematic Review

Plant diseases remain a threat to global agricultural productivity, food security and livelihoods, especially in developing countries where the availability of experts in agriculture is still limited. The recent progress in AI, particularly deep learning and computer vision, has ushered in new possibilities for automated plant disease diagnosis, especially for plant image-based systems. This paper provides a systematic review of the deep learning methods employed for plant disease diagnosis, highlighting CNN-based methods, the application of transfer learning, explainable AI (XAI) methods and deployment issues. In the framework of PRISMA 2020, the relevant peer reviewed literature from 2016 to 2025 was systematically identified, screened and analysed on the most important academic databases. The review compared some of the most popular architectures such as GoogLeNet, DenseNet-121, MobileNetV2, EfficientNet, Attention-CNNs and Vision Transformers. Results showed very high classification accuracy in controlled lab conditions with DenseNet-121 achieving ~99.75% accuracy with good computational efficiency. But it also revealed a big gap between the lab and the field, mainly due to environmental variations, domain shifts, and dependence on datasets. Some innovative and emerging technologies like explainable AI, hyperspectral imaging, few-shot learning, and lightweight mobile architectures showed promise of enhancing the interpretability, early detection of disease, and the use of smart phones in low-resource agricultural settings. In conclusion, the study suggests that in order to be implementable in the field, future intelligent agricultural diagnosis systems must be able to balance predictive accuracy, explainability, computational efficiency and field adaptability. The results enrich the existing knowledge on precision agriculture and serve as useful information for researchers, agricultural technologists, and policymakers working on the creation of AI-based systems for crop protection.

GRAPEVINE DISEASE DETECTION USING AN OPTIMIZED DEEP NEURAL NETWORK FOR SMART AGRICULTURE

This study introduces a lightweight convolutional neural network architecture specifically designed for accurately and efficiently detecting grapevine leaf diseases—including Black Rot, ESCA, and Leaf Blight—based on image classification.

Early Detection of Disease in Millet Crops Using Transfer Learning

Experimental data show that the proposed EfficientNetB0V2 + CNN model achieves superior performance, with higher accuracy and better precision, recall and F1 Score across all classes, highlighting the effectiveness of the suggested approach in detecting complex disease patterns.

Deep Learning Architectures for Fruit and Leaf Disease Detection: A Critical Analysis of Methods, Metrics, Challenges, and Future Research Directions

A Comprehensive Survey on Machine Learning and Deep Learning Approaches for Potato Leaf Disease Detection