Huanglongbing (HLB) disease detection using drone imagery and deep learning neural networks for early management of HLB - Kalbande, Vishal Dashrathrao; Phanomsophon, Thitima; Hongwiangjan, Jeerayut; Sripinyowanich Jongyingcharoen, Jiraporn; Sirisomboon, Panmanas; Shreshta, Bim Prasad; Mani, Indra; Shinde, Gopal U.; Asewar, B. V.; Jagtap, G. P. (My University, 2026)
In the modern world, machine learning and artificial intelligence have become the foundation of the digital revolution and have a significant role in daily life. It is adopted in various applications, such as object detection, recognition and classification. This research aimed to use deep learning algorithms for the detection of Huanglongbing (HLB)-infected disease, healthy and background patch in citrus orchard. HLB-infected patches need to be identified for appropriate treatments of spraying. However, farmers are unaware of infections on plant leaves and therefore adopt manual disease identification methods. This method results in loss of productivity as the infection spreads throughout the field. However, due to a lack of required facilities, instant identification needs to be improved in many aspects of the agricultural sector. To conduct this research, firstly, a dataset was created that contained drone images of citrus orchards that was categorized into three classes, specifically (i) Healthy, (ii) HLB-infected and (iii) Background. Under this research, 6000 image patches of citrus orchard were collected and categorized 2000 images in each class based on appropriate labels. The next step was to train the deep learning models to identify Healthy, HLB-infected and Background. In this research three models viz. EfficientNetV2B0, DenseNet-121 and ResNet-50 were trained to detect the HLB infection patch in the citrus orchard. The models exhibited exceptional performance, with ResNet-50 achieving the highest overall accuracy of 89%, followed by EfficientNet-V2B0 (87%) and DenseNet-121 (85%). ResNet-50 demonstrated superior balanced performance with macro-average precision of 90%, recall of 89%, and F1-score of 89%. The generated HLB detection map effectively visualized disease distribution patterns, identifying 31% of patches as HLB-infected, 7% as Healthy and 62% as Background across the orchard grid. This research establishes a reliable framework for large-scale, automated citrus disease monitoring that enables precision agriculture interventions, potentially reducing economic losses and optimizing resource allocation in citrus cultivation through early and accurate HLB disease detection.
Keywords: Deep learning; Densenet-121; EfficientNet-V2B0; Hlb; Resnet-50