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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.

Keamanan dalam operasional pelabuhan sangat ditentukan oleh kemampuan untuk mengawasi dan melindungi kontainer, yang meskipun berperan sebagai sarana utama perlindungan, masih rentan terhadap kerusakan. Rekaman CCTV di pelabuhan tidak dapat mengenali jenis kendaraan, seperti membedakan antara truk yang membawa kontainer dan yang tidak, serta tidak mampu mendeteksi kerusakan pada kontainer secara otomatis. Dalam mengatasi masalah ini, studi ini mengembangkan sebuah sistem untuk mendeteksi kontainer dan mengklasifikasikan jenis-jenis kerusakan seperti kerusakan struktural, korosi, depos, cacat pada cat, pembengkakan, serta masalah pada pintu kontainer, dengan memanfaatkan teknik machine learning. Menggunakan pendekatan Convolutional Neural Network (CNN) yang ditingkatkan dengan metode transferslearning dari DeepsConvolutional NeuralsNetworks(DCNN), penelitian ini memberikan solusi analitis untuk citra yang diperoleh. Dataset yang terdiri dari 3000 gambar kontainer sisi depan dan belakang dikategorikan secara manual melalui platform Roboflow. Model YOLOv7 yang terlatih pada dataset tersebut mampu mendeteksi kontainer dengan Skor F1 Terboboti mencapai 90%. Untuk tahap klasifikasi kerusakan kontainer, citra yang telah di-crop berdasarkan output YOLOv7 dianalisis kembali menggunakan model EfficientNetV2S dan ConvNeXtBase. Kedua model tersebut, dengan pemanfaatan transfer learning, menunjukkan performa yang dengan Weighted Average F1 Score berturut-turut sebesar 66% dan 72%. Penelitian ini membuka jalur baru untuk peningkatan keamanan dan pemeliharaan kontainer melalui penerapan model pengenalan gambar yang inovatif di lingkungan pelabuhan.