Amidst growing food production demands, early plant disease detection is
essential to safeguard crops; this study proposes a visual machine learning
approach for plant disease detection, harnessing RGB and NIR data collected in
real-world conditions through a JAI FS-1600D-10GE camera to build an RGBN
dataset. A two-stage early plant disease detection model with YOLOv8 and a
sequential CNN was used to train on a dataset with partial labels, which showed
a 3.6% increase in mAP compared to a single-stage end-to-end segmentation
model. The sequential CNN model achieved 90.62% validation accuracy utilising
RGBN data. An average of 6.25% validation accuracy increase is found using RGBN
in classification compared to RGB using ResNet15 and the sequential CNN models.
Further research and dataset improvements are needed to meet food production
demands.

通过 JAI FS-1600D-10GE 相机收集在实际环境中的红绿蓝近红外数据，并构建一个 RGBN 数据集，本研究提出了一种用于植物疾病检测的视觉机器学习方法。使用 YOLOv8 和顺序 CNN 的两阶段早期植物疾病检测模型在具有部分标签的数据集上进行训练，与单阶段端到端分割模型相比，mAP 提高了 3.6%。顺序 CNN 模型利用 RGBN 数据实现了 90.62% 的验证准确率。在分类方面，与使用 ResNet15 和顺序 CNN 模型的 RGB 相比，使用 RGBN 获得了平均 6.25% 的验证准确率提高。需要进一步的研究和数据集改进来满足粮食生产需求。

使用自定义数据集的机器学习方法检测拉布拉多豆上的螨虫

Detection of Spider Mites on Labrador Beans through Machine Learning  Approaches Using Custom Datasets

The research introduces a novel plant disease detection model based on
Convolutional Neural Networks (CNN) for plant image classification, marking a
significant contribution to image categorization. The innovative training
approach enables a streamlined and efficient system implementation. The model
classifies two distinct plant diseases into four categories, presenting a novel
technique for plant disease identification. In Experiment 1, Inception-V3,
Dense-Net-121, ResNet-101-V2, and Xception models were employed for CNN
training. The newly created plant disease image dataset includes 1963 tomato
plant images and 7316 corn plant images from the PlantVillage dataset. Of
these, 1374 tomato images and 5121 corn images were used for training, while
589 tomato images and 2195 corn images were used for testing/validation.
Results indicate that the Xception model outperforms the other three models,
yielding val_accuracy values of 95.08% and 92.21% for the tomato and corn
datasets, with corresponding val_loss values of 0.3108 and 0.4204,
respectively. In Experiment 2, CNN with Batch Normalization achieved disease
detection rates of approximately 99.89% in the training set and val_accuracy
values exceeding 97.52%, accompanied by a val_loss of 0.103. Experiment 3
employed a CNN architecture as the base model, introducing additional layers in
Model 2, skip connections in Model 3, and regularizations in Model 4. Detailed
experiment results and model efficiency are outlined in the paper's sub-section
1.5. Experiment 4 involved combining all corn and tomato images, utilizing
various models, including MobileNet (val_accuracy=86.73%), EfficientNetB0
(val_accuracy=93.973%), Xception (val_accuracy=74.91%), InceptionResNetV2
(val_accuracy=31.03%), and CNN (59.79%). Additionally, our proposed model
achieved a val_accuracy of 84.42%.

本研究基于卷积神经网络 (CNN) 引入一种新颖的植物病害检测模型，用于植物图像分类，对图像分类做出了重要的贡献。创新的训练方法使系统实施变得简化高效。该模型将两种不同的植物病害分类为四个类别，提出了一种新颖的植物病害识别技术。实验结果表明，Xception 模型优于其他三个模型，在番茄数据集和玉米数据集上分别获得了 95.08% 和 92.21% 的准确率，对应的损失函数值分别为 0.3108 和 0.4204。CNN 结合批归一化实现了大约 99.89% 的训练集病害检测率和超过 97.52% 的验证准确率，伴随着 0.103 的验证损失。在模型的基础上引入额外层、跳跃连接和正则化，分别对应实验 2、实验 3 和实验 4 的 CNN 架构。综合应用各种模型，包括 MobileNet、EfficientNetB0、Xception、InceptionResNetV2 和 CNN，实现了对所有玉米和番茄图像的检测，其中模型的验证准确率为 84.42%。

基于图像的西红柿和玉米叶病害检测：一项深度对比实验

On the Image-Based Detection of Tomato and Corn leaves Diseases : An  in-depth comparative experiments

For applications like plant disease detection, usually, a model is trained on
publicly available data and tested on field data. This means that the test data
distribution is not the same as the training data distribution, which affects
the classifier performance adversely. We handle this dataset shift by ensuring
that the features are learned from disease spots in the leaf or healthy
regions, as applicable. This is achieved using a faster Region-based
convolutional neural network (RCNN) as one of the solutions and an
attention-based network as the other. The average classification accuracies of
these classifiers are approximately 95% while evaluated on the test set
corresponding to their training dataset. These classifiers also performed
equivalently, with an average score of 84% on a dataset not seen during the
training phase.

通过使用区域卷积神经网络（RCNN）和基于注意力机制的网络，从叶片上的病斑或健康区域学习特征的方式，处理了训练数据分布与测试数据分布不一致所带来的分类器性能问题，测试集上的平均分类准确率约为 95％，在训练阶段未见过的数据集上平均得分为 84％。