Added soybean insect classification dataset along with added the miss…

README.md

@@ -203,7 +203,7 @@ to your desired specification.
 [cucumber_disease_classification](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/cucumber_disease_classification.md) | Image Classification | 7689 |
 [iNatAg](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/iNatAg.md) | Image Classification | 4720903 |
 [iNatAg-mini](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/iNatAg-mini.md) | Image Classification | 560844 |
-
+[soybean_insect_classification](https://github.com/Project-AgML/AgML/blob/main/docs/datasets/soybean_insect_classification.md) | Image Classification | 6410 |
 
 ## iNatAg and iNatAg-mini
 
@@ -218,6 +218,7 @@ loader = agml.data.AgMLDataLoader.from_parent("iNatAg", filters={"family_name":
 loader = agml.data.AgMLDataLoader.from_parent("iNatAg", filters={"common_name": "..."})
 ```
 
+
 ## Usage Information
 
 ### Using Public Agricultural Data
@@ -248,5 +249,4 @@ a bug or feature that you would like to see implemented, please don't hesitate t
 See the [contributing guidelines](/CONTRIBUTING.md) for more information.
 
 ## Funding
-This project is partly funded by the [National AI Institute for Food Systems](https://aifs.ucdavis.edu).
-
+This project is partly funded by the [National AI Institute for Food Systems](https://aifs.ucdavis.edu).

agml/_assets/public_datasources.json

@@ -1057,6 +1057,11 @@
             "continent": "worldwide",
             "country": "worldwide"
         },
+        "sensor_modality": "rgb",
+        "real_synthetic": "real",
+        "platform": "handheld/ground",
+        "input_data_format": "jpg",
+        "annotation_format": "image",
         "n_images": "42",
         "docs_url": "https://data.nal.usda.gov/dataset/data-multi-species-fruit-flower-detection-using-refined-semantic-segmentation-network",
         "external_image_sources": [],
@@ -2462,5 +2467,40 @@
         },
         "parent_dataset": "",
         "extra_metadata": {}
+    },
+    "soybean_insect_classification": {
+        "ml_task": "image_classification",
+        "ag_task": "pest_classification",
+        "location": {
+            "continent": "south_america",
+            "country": "brazil"
+        },
+        "sensor_modality": "rgb",
+        "real_synthetic": "real",
+        "platform": "uav",
+        "input_data_format": "jpg",
+        "annotation_format": "directory_names",
+        "n_images": "6410",
+        "docs_url": "https://data.mendeley.com/datasets/bycbh73438/1",
+        "classes": {
+            "0": "Caterpillar",
+            "1": "Diabrotica_speciosa",
+            "2": "Healthy"
+        },
+        "external_image_sources": [],
+        "parent_dataset": "",
+        "extra_metadata": {},
+        "stats": {
+            "mean": [
+                0.4129256308078766,
+                0.515846848487854,
+                0.3347107470035553
+            ],
+            "std": [
+                0.22007715702056885,
+                0.22993944585323334,
+                0.20302338898181915
+            ]
+        }
     }
 }

agml/_assets/source_citations.json

@@ -262,5 +262,9 @@
     "cucumber_disease_classification": {
         "license": "CC BY 4.0",
         "citation": "Sultana, Nusrat; Shorif, Sumaita Binte ; Akter, Morium ; Uddin, Mohammad Shorif  (2022), “Cucumber Disease Recognition Dataset”, Mendeley Data, V1, doi: 10.17632/y6d3z6f8z9.1"
+    },
+    "soybean_insect_classification": {
+        "license": "CC BY 4.0",
+        "citation": "Mignoni, Maria Eloisa (2021), “Images of Soybean Leaves”, Mendeley Data, V1, doi: 10.17632/bycbh73438.1"
     }
 }

agml/_internal/preprocess.py

@@ -1585,6 +1585,9 @@ def sunflower_disease_classification(self, dataset_name):
     def cucumber_disease_classification(self, dataset_name):
         pass
 
+    def soybean_insect_classification(self, dataset_name):
+        pass
+
 if __name__ == '__main__':
     # Initialize program arguments.
     ap = argparse.ArgumentParser()

docs/datasets/iNatAg-mini.md

@@ -10,10 +10,7 @@ It consists of 2,959 species with a breakdown of 1,986 crop species and 973 weed
 from pprint import pprint
 import agml
 
-datasets = agml.data.public_data_sources()
-iNatAg_mini_datasets = [ds._name.replace("iNatAg-mini/","") for ds in datasets if "iNatAg-mini" in ds._name]
-
-print(iNatAg_mini_datasets)
+print(agml.data.public_data_sources(parent_dataset='iNatAg-mini'))
 ```
 
 ## Loading iNatAg-mini dataset

docs/datasets/iNatAg.md

@@ -8,10 +8,7 @@ iNatAg is a large-scale dataset derived from the iNaturalist dataset, designed f
 from pprint import pprint
 import agml
 
-datasets = agml.data.public_data_sources()
-iNatAg_datasets = [ds._name.replace("iNatAg/","") for ds in datasets if "iNatAg" in ds._name]
-
-print(iNatAg_datasets)
+print(agml.data.public_data_sources(parent_dataset='iNatAg'))
 ```
 
 ## Loading iNatAg dataset

docs/datasets/soybean_insect_classification.md

@@ -0,0 +1,27 @@
+
+# `soybean_insect_classification`
+
+## Dataset Metadata
+
+| Metadata | Value |
+| --- | --- |
+| **Machine Learning Task** | image_classification |
+| **Agricultural Task** | pest_classification |
+| **Location** | Brazil, South America |
+| **Sensor Modality** | RGB |
+| **Real or Synthetic** | real |
+| **Platform** | uav |
+| **Input Data Format** | JPG |
+| **Annotation Format** | directory_names |
+| **Number of Images** | 6410 |
+| **Documentation** | https://data.mendeley.com/datasets/bycbh73438/1 |
+| **Classes** | Caterpillar, Diabrotica_speciosa, Healthy |
+| **Parent Dataset** | None |
+| **Extra Metadata** | {} |
+| **Stats/Mean** | [0.413, 0.516, 0.335] |
+| **Stats/Standard Deviation** | [0.22, 0.23, 0.203] |
+
+
+## Examples
+
+![Example Images for soybean_insect_classification](https://github.com/Project-AgML/AgML/blob/main/docs/sample_images/soybean_insect_classification_examples.png)