School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamilnadu, India.
PLoS One. 2024 Mar 25;19(3):e0301174. doi: 10.1371/journal.pone.0301174. eCollection 2024.
Crop losses caused by diseases and pests present substantial challenges to global agriculture, with groundnut crops particularly vulnerable to their detrimental effects. This study introduces the Groundnut Vision Transformer (GNViT) model, a novel approach that harnesses a pre-trained Vision Transformer (ViT) on the ImageNet dataset. The primary goal is to detect and classify various pests affecting groundnut crops. Rigorous training and evaluation were conducted using a comprehensive dataset from IP102, encompassing pests such as Thrips, Aphids, Armyworms, and Wireworms. The GNViT model's effectiveness was assessed using reliability metrics, including the F1-score, recall, and overall accuracy. Data augmentation with GNViT resulted in a significant increase in training accuracy, achieving 99.52%. Comparative analysis highlighted the GNViT model's superior performance, particularly in accuracy, compared to state-of-the-art methodologies. These findings underscore the potential of deep learning models, such as GNViT, in providing reliable pest classification solutions for groundnut crops. The deployment of advanced technological solutions brings us closer to the overarching goal of reducing crop losses and enhancing global food security for the growing population.
病虫害导致的作物损失对全球农业构成了重大挑战，落花生作物尤其容易受到其不利影响。本研究引入了花生视觉Transformer（GNViT）模型，这是一种利用在 ImageNet 数据集上预训练的 Vision Transformer（ViT）的新方法。主要目标是检测和分类影响落花生作物的各种害虫。使用来自 IP102 的综合数据集，包括蓟马、蚜虫、粘虫和地老虎等害虫，进行了严格的训练和评估。使用可靠性指标，包括 F1 分数、召回率和整体准确性，评估了 GNViT 模型的有效性。使用 GNViT 进行数据扩充导致训练准确性显著提高，达到 99.52%。与最先进的方法相比，对比分析突出了 GNViT 模型在准确性方面的卓越性能。这些发现强调了深度学习模型（如 GNViT）在为落花生作物提供可靠的害虫分类解决方案方面的潜力。先进技术解决方案的部署使我们更接近减少作物损失和提高全球粮食安全以满足不断增长的人口的总体目标。
