[1/3 - anomaly detection] [1/2 - KNN classification] Batch upload dataset to Qdrant (crops dataset)

### For anomaly detection
**1. This is the first pipeline to upload (crops) dataset to Qdrant's collection.**
2. The second pipeline is to set up cluster (class) centres in this Qdrant collection & cluster (class) threshold scores.
3. The third is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant (crops) collection.

### For KNN (k nearest neighbours) classification
**1. This is the first pipeline to upload (lands) dataset to Qdrant's collection.**
2. The second is the KNN classifier tool, which takes any image as input and classifies it based on queries to the Qdrant (lands) collection.

### To recreate both
You'll have to upload [crops](https://www.kaggle.com/datasets/mdwaquarazam/agricultural-crops-image-classification) and [lands](https://www.kaggle.com/datasets/apollo2506/landuse-scene-classification) datasets from Kaggle to your own Google Storage bucket, and re-create APIs/connections to [Qdrant Cloud](https://qdrant.tech/documentation/quickstart-cloud/) (you can use **Free Tier** cluster), Voyage AI API & Google Cloud Storage

**In general, pipelines are adaptable to any dataset of images**