Classification of Weeds Using Neural Network Algorithms and Image Classifiers

In agriculture it is very important to differentiate between crop seedling and weed from the farms, the traditional means to classify were mostly relies on manual methods which is not time and cost efficient. Automated Deep learning-based Weed classification plays crucial role to solve this in agriculture sector for effective crop yield and weed management. Also this study could help for precision agriculture, cost reduction and time efficiency in the field. In this research paper, we present several deep learning-based approach for automated classification between weed and crop seedlings. For the classification task we used popular image classification algorithms, which includes Convolution Neural Network (CNN), Artificial Neural Network (ANN), Support VectorMachine (SVM), K-Nearest Neighbors (KNN). The study utilizes a diverse dataset between crop seedling and weed image data. We also demonstrated the results of deep learning based algorithm and compare them to find the best for the classification task. The work highlights the capability of deep learning-based model for efficient weed management, aiding farmers in identifying and controlling weeds while growing crop seedlings, and hence leads to improved productivity.