Revolutionizing Agriculture: Machine and Deep Learning Solutions for Enhanced Crop Quality and Weed Control

Abstract

Agricultural systems are being revolutionized due to emerging technologies that aim to make improvements in the traditional agriculture system. The major goal is not just to enhance agricultural output per hectare but also to enhance crop quality while protecting the natural environment. Weeds pose a significant threat to crops as they consume nutrients, water, and light, thereby reducing crop productivity. Spraying the entire field uniformly to control weeds not only incurs high costs but also has adverse environmental effects. To address the limitations of conventional weed control methods, in this research, Machine Learning (ML) and Deep Learning (DL) based techniques are proposed to identify and categorize weeds in crops. For ML-based techniques, several statistical and texture-based features are extracted, including central image and Hu moments, mean absolute deviation, Shannon entropy, gray level co-occurrence matrix (GLCM) and local binary patterns (LBP), contrast, energy, homogeneity, dissimilarity, correlation, and summarized local binary pattern histogram. YOLOv8m is employed to identify weeds and for weed classification, features extracted from two standard benchmark datasets, CottonWeedID15 and Earlycrop-weed are fed to Support Vector Machine (SVM), Random Forest, and Artificial Neural Network (ANN) while employing Synthetic Minority Oversampling Technique (SMOTE) to balance the classes. In addition to ML-based techniques, Deep learners such as VGG16, VGG19, Xception, DenseNet121, DenseNet169, DenseNet201, and ConvNeXtBase are trained on raw data with balanced classes for automated feature extraction and classification. Among the ML-based techniques, SVM with a polynomial kernel achieves 99.5% accuracy on the early crop weed dataset, and Artificial neural network attains 89% accuracy on the Cottonweedid15 dataset. Meanwhile, the combined employment of ConvNeXt and Random Forest results in the highest accuracy among DLs, specifically 98% on the early crop weed dataset and 90% on the Cottonweedid15 dataset. The high accuracy achieved underscores the practical viability of these methods, offering a sustainable and cost-effective solution for modern agriculture.

Keywords

• ConvNeXtBase
• DenseNet
• generative AI
• smart agriculture
• VGG
• Xception