Department of Computer Science and Information Systems, BITS Pilani, Pilani Campus, Pilani, Rajasthan, 333031, India.
Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, 700032, India.
Sci Rep. 2024 Jul 5;14(1):15537. doi: 10.1038/s41598-024-66543-7.
Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. Hence, continuous health monitoring of plant is very crucial for handling plant stress. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. Furthermore, a GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, called Plant Nutrition Deficiency and Disease Network (PND-Net), has been evaluated on two public datasets for nutrition deficiency, and two for disease classification using four backbone CNNs. The best classification performances of the proposed PND-Net are as follows: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40 : 95.50%, and BreakHis 100 : 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy). Also, the proposed method has been evaluated using five-fold cross validation and achieved improved performances on these datasets. Clearly, the proposed PND-Net effectively boosts the performances of automated health analysis of various plants in real and intricate field environments, implying PND-Net's aptness for agricultural growth as well as human cancer classification.
如果能够在早期识别和检测各种植物营养缺乏症和疾病，就可以提高作物产量。因此，持续监测植物的健康状况对于处理植物压力非常关键。深度学习方法已经证明，它们在从叶片的视觉症状自动检测植物疾病和营养缺乏方面具有优越的性能。本文提出了一种新的深度学习方法，用于使用图卷积网络（GNN）对植物营养缺乏症和疾病进行分类，该方法在基础卷积神经网络（CNN）上进行了扩展。有时，全局特征描述符可能无法捕获患病叶片的重要区域，从而导致疾病分类不准确。为了解决这个问题，区域特征学习对于整体特征聚合至关重要。在这项工作中，使用空间金字塔池化在多尺度上探索基于区域的特征汇总，以进行有区分力的特征表示。此外，开发了一个 GCN 来学习更精细的细节，以便对植物疾病和营养不足进行分类。所提出的方法称为植物营养缺乏和疾病网络（PND-Net），已在两个公共营养缺乏数据集和两个公共疾病分类数据集上使用四个骨干 CNN 进行了评估。所提出的 PND-Net 的最佳分类性能如下：（a）香蕉和咖啡营养缺乏的 90.00%和 90.54%；（b）马铃薯疾病的 96.18%和 PlantDoc 数据集的 84.30%使用 Xception 骨干。此外，还进行了其他泛化实验，所提出的方法在两个公共数据集上达到了最先进的性能，即乳腺癌组织病理学图像分类（BreakHis 40 ：95.50%，BreakHis 100 ：96.79%准确率）和宫颈癌单细胞巴氏涂片图像分类（SIPaKMeD：99.18%准确率）。此外，还使用五折交叉验证对所提出的方法进行了评估，并在这些数据集上取得了改进的性能。显然，所提出的 PND-Net 有效地提高了各种植物在真实和复杂田间环境中的自动健康分析性能，这意味着 PND-Net 适合农业增长和人类癌症分类。
