The tomato is one of the most important fruits on earth. It plays an
important and useful role in the agricultural production of any country. This
research propose a novel smart technique for early detection of late blight
diseases in tomatoes. This work improve the dataset with an increase in images
from the field (the Plant Village dataset) and proposed a hybrid algorithm
composed of support vector machines (SVM) and histogram-oriented gradients
(HOG) for real-time detection of late blight tomato disease. To propose a
HOG-based SVM model for early detection of late blight tomato leaf disease. To
check the performance of the proposed model in terms of MSE, accuracy,
precision, and recall as compared to Decision Tree and KNN. The integration of
advanced technology in agriculture has the potential to revolutionize the
industry, making it more efficient, sustainable, and profitable. This research
work on the early detection of tomato diseases contributes to the growing
importance of smart farming, the need for climate-smart agriculture, the rising
need to more efficiently utilize natural resources, and the demand for higher
crop yields. The proposed hybrid algorithm of SVM and HOG has significant
potential for the early detection of late blight disease in tomato plants. The
performance of the proposed model against decision tree and KNN algorithms and
the results may assist in selecting the best algorithm for future applications.
The research work can help farmers make data-driven decisions to optimize crop
yield and quality while also reducing the environmental impact of farming
practices.

本研究提出了一种使用 SVM 和 HOG 算法的混合模型，可以实现对番茄晚疫病的早期检测，对于智能农业的发展与推广具有重要意义。

基于直方图定向梯度支持向量机的番茄晚疫病早期检测

Early Detection of Late Blight Tomato Disease using Histogram Oriented  Gradient based Support Vector Machine

Most object detection methods operate by applying a binary classifier to
sub-windows of an image, followed by a non-maximum suppression step where
detections on overlapping sub-windows are removed. Since the number of possible
sub-windows in even moderately sized image datasets is extremely large, the
classifier is typically learned from only a subset of the windows. This avoids
the computational difficulty of dealing with the entire set of sub-windows,
however, as we will show in this paper, it leads to sub-optimal detector
performance.
In particular, the main contribution of this paper is the introduction of a
new method, Max-Margin Object Detection (MMOD), for learning to detect objects
in images. This method does not perform any sub-sampling, but instead optimizes
over all sub-windows. MMOD can be used to improve any object detection method
which is linear in the learned parameters, such as HOG or bag-of-visual-word
models. Using this approach we show substantial performance gains on three
publicly available datasets. Strikingly, we show that a single rigid HOG filter
can outperform a state-of-the-art deformable part model on the Face Detection
Data Set and Benchmark when the HOG filter is learned via MMOD.

本文介绍了一种新的方法 Max-Margin Object Detection（MMOD）用于在图像中学习检测物体，该方法不进行子采样，而是优化所有子窗口，并且可以用于改进任何线性学习参数的物体检测方法，本文证明了该方法在三个公开数据集上具有显著的性能优势，并且在 MMOD 的学习下，单个刚性 HOG 滤波器可以比可变形部件模型在人脸检测数据集和基准测试中表现更好。

最大间隔目标检测

Max-Margin Object Detection

In the last two years, convolutional neural networks (CNNs) have achieved an
impressive suite of results on standard recognition datasets and tasks.
CNN-based features seem poised to quickly replace engineered representations,
such as SIFT and HOG. However, compared to SIFT and HOG, we understand much
less about the nature of the features learned by large CNNs. In this paper, we
experimentally probe several aspects of CNN feature learning in an attempt to
help practitioners gain useful, evidence-backed intuitions about how to apply
CNNs to computer vision problems.

本文探讨了卷积神经网络在计算机视觉问题中的特征学习，主要关注 CNNs 和工程化表示（如 SIFT 和 HOG）之间的差异。