CNN Model for Smart Agriculture
Prof. Shivarudraiah
B Dept. of ECE BMSIT&M
Bengaluru, India shivab@bmsit.in
Ashish
Dept. of ECE BMSIT&M
Bengaluru, India shankerashish9@gmail.com
Avani Suresh Kengal
Dept. of ECE BMSIT&M
Bengaluru, India avanikengal21@gmail.com
Kuchipudi Sowmya Jasmine
Dept. of ECE BMSIT&M
Bengaluru, India sowmyakuchipudi16@gmail.com
Hemavathi KR
Dept. of ECE BMSIT&M
Bengaluru, India hemavathi242@gmail.com
Abstract— Precision farming is being revolutionized by the integration of innovative machine learning and computer vision methods. Identifying and classifying weeds and crops accurately remains a major challenge in this field, which has a direct effect on optimizing the yield as well as sustainability. In this work, an approach to smart weed detection based on deep learning using Convolutional Neural Networks (CNN) for feature learning followed by comparison of classifiers to select the best-performing model is introduced. In our research, InceptionV3 was utilized to extract features, and four classifiers—SoftMax, Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)— were compared. Among them, the Random Forest classifier performed better than others with a validation accuracy of 99.57% and an F1 score of 0.99. Extending the successful application of crop-weed detection, the model was transferred to a new application: forest fire detection. Employing the same CNN- based feature extraction pipeline and Random Forest classification, our system showed high accuracy on a forest fire dataset. In addition, we implemented a real-time detection system using webcam feeds with a processing speed of around 30 frames per second, making practical deployment for environmental monitoring possible. This study not only confirms the efficacy of the union of CNNs and ensemble learning but also exemplifies the versatility of the model architecture in both agricultural and environmental contexts.
Index Terms— Convolutional Neural Networks (CNN), InceptionV3, Random Forest, Weed Detection, Crop Classification, Forest Fire Detection, Real-Time Image Processing, Machine Learning, Deep Learning, Precision Agriculture, Environmental Monitoring, Feature Extraction, Webcam Detection.
