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The tomato is one of the most important fruits on earth. It plays animportant and useful role in the agricultural production of any country. Thisresearch propose a novel smart technique for early detection of late blightdiseases in tomatoes. This work improve the dataset with an increase in imagesfrom the field (the Plant Village dataset) and proposed a hybrid algorithmcomposed of support vector machines (SVM) and histogram-oriented gradients(HOG) for real-time detection of late blight tomato disease. To propose aHOG-based SVM model for early detection of late blight tomato leaf disease. Tocheck the performance of the proposed model in terms of MSE, accuracy,precision, and recall as compared to Decision Tree and KNN. The integration ofadvanced technology in agriculture has the potential to revolutionize theindustry, making it more efficient, sustainable, and profitable. This researchwork on the early detection of tomato diseases contributes to the growingimportance of smart farming, the need for climate-smart agriculture, the risingneed to more efficiently utilize natural resources, and the demand for highercrop yields. The proposed hybrid algorithm of SVM and HOG has significantpotential for the early detection of late blight disease in tomato plants. Theperformance of the proposed model against decision tree and KNN algorithms andthe results may assist in selecting the best algorithm for future applications.The research work can help farmers make data-driven decisions to optimize cropyield and quality while also reducing the environmental impact of farmingpractices.

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