resume: organized crop disease dataset

README.md

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+---
+license: other
+language:
+- en
+task_categories:
+- image-classification
+tags:
+- plant-disease
+- agriculture
+- crops
+- leaf
+- banana
+- tomato
+- potato
+- lemon
+- tea
+- rice
+- watering
+- irrigation
+- treatment
+size_categories:
+- 100K<n<1M
+format: image
+modality:
+- image
+library_tags:
+- pytorch
+- tensorflow
+---
+
+# CropHelth — Unified Crop Disease & Watering Dataset
+
+**~125,000 plant leaf images across 109 disease/healthy classes for 26 crops, plus a per-image provenance manifest, a treatment knowledge base, and per-crop watering recommendation rules.**
+
+Built for the CropHelth project: an image classifier that outputs code-based JSON (e.g. `{"status": {"code": "potato_lb"}, "treatment": {"pill_code": ...}}`) and an environment-based watering decision engine.
+
+## Layout
+
+```
+images/<plant>_<ill>/NNNNNN.jpg     # 109 classes, ~125k images (the training set)
+_source_manifest.csv                 # per-image provenance: target_code, source, original_path
+detection/                           # PlantDoc object-detection dataset (VOC: .jpg + .xml, TRAIN/TEST)
+knowledge/
+  names.json                         # class code -> human-readable message
+  treatments.json                    # class code -> {treatment: {pill_code, pill_name}, agronomy}
+  watering_rules.json                # per-crop thresholds for the watering decision engine
+  watering_recommendations.csv       # human-readable watering reference table (ph, temp, moisture, practice)
+labels.json                          # class list + code->index + per-class counts
+README.md
+```
+
+### Class code scheme
+
+`<plant>_<ill>` — e.g. `potato_late_blight`, `banana_sigatoka`, `tea_helopeltis`, `tomato_healthy`.
+- `*_healthy` = healthy class for that plant (the model's "healthy" output).
+- Plant = first token of the code.
+- 109 classes, one folder per class — drop `images/` straight into any classification pipeline (ImageFolder / the CropHelth trainer both work as-is).
+
+## Sources (all open source)
+
+| Source | DOI / URL | Contribution |
+|---|---|---|
+| Mendeley `tywbtsjrjv` — "Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep CNN" (PlantVillage) | https://data.mendeley.com/datasets/tywbtsjrjv/1 | apple, blueberry, cherry, corn, grape, orange, peach, pepper, potato, raspberry, soybean, squash, strawberry, tomato (~52k) |
+| Mendeley `9tb7k297ff` — BananaLSD | https://data.mendeley.com/datasets/9tb7k297ff/1 | banana (sigatoka, cordana, pestalotiopsis, healthy; Original set only — augmented set excluded to avoid synthetic-duplicate bias) |
+| Mendeley `744vznw5k2` — teaLeafBD | https://data.mendeley.com/datasets/744vznw5k2/4 | tea (7 classes) |
+| Mendeley `j32xdt2ff5` — Tea Sickness | https://data.mendeley.com/datasets/j32xdt2ff5/2 | tea (8 classes, merged with teaLeafBD) |
+| Mendeley `6243z8r6t6` — Multi-Crop Disease (roboflow) | https://data.mendeley.com/datasets/6243z8r6t6/1 | banana, cauliflower, chilli, peanut, radish (~22k; class assigned via YOLO label files) |
+| Mendeley `643f5bbc2t` — BDLemonLeaf | https://data.mendeley.com/datasets/643f5bbc2t/6 | lemon (13 classes, Raw Image set) |
+| Mendeley `8d9fv6kpt3` — Large-Scale Lemon Leaf Disease & Pest | https://data.mendeley.com/datasets/8d9fv6kpt3/1 | lemon (18 classes, merged with BDLemonLeaf) |
+| Mendeley `c5yvn32dzg` — RoCoLe (robusta coffee) | https://data.mendeley.com/datasets/c5yvn32dzg/2 | coffee healthy/unhealthy (labels from the shipped Labelbox CSV) |
+| Mendeley `22p2vcbxfk` — Groundnut (peanut) | https://data.mendeley.com/datasets/22p2vcbxfk/3 | peanut (5 classes, Raw_Data) |
+| Mendeley `7vpdrbdkd4` — Nutrient-deficient banana | https://data.mendeley.com/datasets/7vpdrbdkd4/2 | banana nutrient deficiencies (7 classes, RAW set only) |
+| Mendeley `g7xnn2bm4g` — Nitrogen deficiency in maize | https://data.mendeley.com/datasets/g7xnn2bm4g/1 | maize N0/N7/NF (3 classes) |
+| GitHub `pratikkayal/PlantDoc-Dataset` | https://github.com/pratikkayal/PlantDoc-Dataset | 28 classes (train+test merged) |
+| GitHub `pratikkayal/PlantDoc-Object-Detection-Dataset` | https://github.com/pratikkayal/PlantDoc-Object-Detection-Dataset | `detection/` (VOC format, separate task) |
+| Harvard Dataverse `LQUWXW` — Bananas TZ (Tanzania) | https://doi.org/10.7910/DVN/LQUWXW | banana black sigatoka, fusarium wilt, healthy (~15k large photos) |
+
+**Excluded by design:** `Background_without_leaves` (PlantVillage, not a plant class), augmented/synthetic image sets (our trainer does its own augmentation), and the Zenodo **DiaMOS** pear dataset (12.5 GB single archive — could not be processed in the build environment; add it with the same pipeline).
+
+**Note:** the IEEE DataPort *Paddy Doctor* (rice) dataset requires a logged-in IEEE account to download — it could not be included automatically. Rice classes should be added the same way when you download it.
+
+## Watering decision data
+
+`knowledge/watering_rules.json` drives a rule engine (`water_now` / `hold_water` / `check_data`):
+
+- `soil_moisture_pct < min` → `water_now`
+- `soil_moisture_pct > max` → `hold_water` (overwatering risk)
+- no soil moisture given → `check_data`
+- `ph` / `air_temp_c` / `soil_temp_c` outside range → extra reason codes (`ph_low`, `ph_high`, `air_temp_high`, `soil_temp_high`)
+
+Per-crop values (moisture %, pH window, temperature stress limits) in `watering_rules.json` + the readable practice table in `watering_recommendations.csv`. **Values are standard agronomic reference ranges — calibrate them with your own field sensors.** Soil moisture is the primary signal; pH/temperature alone cannot decide irrigation. To replace rules with a learned model, log readings + actual watering actions and train a classifier (see the CropHelth trainer's `train_watering.py`).
+
+## Treatments
+
+`knowledge/treatments.json` maps every non-healthy class to `{treatment: {pill_code, pill_name}, agronomy}`. These are **standard extension-service style reference recommendations for training/testing** — before any real-world use, verify products are registered in your region, follow label rates, rotate FRAC groups, and have a local agronomist review. The model predicts the class code only; treatment text always comes from this file (never model-generated).
+
+## Provenance & licenses
+
+`_source_manifest.csv` maps every image back to its source dataset and original path. Each source keeps its own license (see `detection/LICENSE.txt` for PlantDoc; Mendeley datasets are typically CC-BY/CC0 — check each dataset page). If you redistribute, keep the source attribution above.
+
+## Quick usage (CropHelth trainer)
+
+```bash
+# point the trainer at this dataset
+python scripts/build_labels.py --images-dir <this repo>/images
+python scripts/split.py
+python scripts/train.py
+python scripts/predict.py --image some_leaf.jpg --meta
+python scripts/predict_watering.py --json '{"crop":"potato","soil_moisture_pct":18,"ph":6.1}'
+```

_source_manifest.csv

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detection/LICENSE.txt

@@ -0,0 +1,395 @@
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detection/README.md

@@ -0,0 +1,37 @@
+# PlantDoc: A Dataset for Visual Plant Disease Detection
+
+This repository contains the PlantDoc dataset used for benchmarking classification models in the paper titled "PlantDoc: A Dataset for Visual Plant Disease Detection" which was accepted in the Research Track at ACM India Joint International Conference on Data Science and Management of Data (CoDS-COMAD 2020).
+
+While the cropped dataset can accessed through the following [link](https://github.com/pratikkayal/PlantDoc-Dataset).
+
+## Abstract
+India loses 35% of the annual crop yield due to plant diseases. Early detection of plant diseases remains difficult due to the lack of lab infrastructure and expertise. In this paper, we explore the possibility of computer vision approaches for scalable and early plant disease detection. The lack of availability of sufficiently large-scale non-lab data set remains a major challenge for enabling vision based plant disease detection. Against this background, we present PlantDoc: a dataset for visual plant disease detection. Our dataset contains 2,598 data points in total across 13 plant species and up to 17 classes of diseases, involving approximately 300 human hours of effort in annotating internet scraped images. To show the efficacy of our dataset, we learn 3 models for the task of plant disease classification. Our results show that modelling using our dataset can increase the classification accuracy by up to 31%. We believe that our dataset can help reduce the entry barrier of computer vision techniques in plant disease detection.
+
+## Paper 
+For full paper, refer [Arxiv](https://arxiv.org/abs/1911.10317) and [ACM](https://dl.acm.org/doi/10.1145/3371158.3371196)
+
+## Authors 
+Davinder Singh*, Naman Jain*, Pranjali Jain*, Pratik Kayal*, Sudhakar Kumawat and Nipun Batra
+
+## Bibtex
+```
+@inproceedings{10.1145/3371158.3371196,
+author = {Singh, Davinder and Jain, Naman and Jain, Pranjali and Kayal, Pratik and Kumawat, Sudhakar and Batra, Nipun},
+title = {PlantDoc: A Dataset for Visual Plant Disease Detection},
+year = {2020},
+isbn = {9781450377386},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3371158.3371196},
+doi = {10.1145/3371158.3371196},
+booktitle = {Proceedings of the 7th ACM IKDD CoDS and 25th COMAD},
+pages = {249–253},
+numpages = {5},
+keywords = {Deep Learning, Object Detection, Image Classification},
+location = {Hyderabad, India},
+series = {CoDS COMAD 2020}
+}
+```
+
+## License
+Creative Commons Attribution 4.0 International [Link](https://github.com/pratikkayal/PlantDoc-Dataset/blob/master/LICENSE.txt)

detection/TEST/%2320+Bacterial+Spot+and+Speck.xml

@@ -0,0 +1,23 @@
+<annotation>
+    <folder> tomato leaf bacterial spot </folder>
+    <filename>%2320+Bacterial+Spot+and+Speck.jpg</filename>
+    <size>
+        <width>960</width>
+        <height>720</height>
+        <depth>3</depth>
+    </size>
+    <segmented>0</segmented>
+    <object>
+        <name>Tomato leaf bacterial spot</name>
+        <pose>Unspecified</pose>
+        <truncated>0</truncated>
+        <occluded>0</occluded>
+        <difficult>0</difficult>
+        <bndbox>
+            <xmin>61</xmin>
+            <ymin>196</ymin>
+            <xmax>410</xmax>
+            <ymax>396</ymax>
+        </bndbox>
+    </object>
+</annotation>

detection/TEST/0.xml

@@ -0,0 +1,36 @@
+<annotation>
+    <folder> Tomato Septoria leaf spot  </folder>
+    <filename>0.jpg</filename>
+    <size>
+        <width>480</width>
+        <height>360</height>
+        <depth>3</depth>
+    </size>
+    <segmented>0</segmented>
+    <object>
+        <name>Tomato Septoria leaf spot</name>
+        <pose>Unspecified</pose>
+        <truncated>0</truncated>
+        <occluded>0</occluded>
+        <difficult>0</difficult>
+        <bndbox>
+            <xmin>244</xmin>
+            <ymin>136</ymin>
+            <xmax>394</xmax>
+            <ymax>253</ymax>
+        </bndbox>
+    </object>
+    <object>
+        <name>Tomato Septoria leaf spot</name>
+        <pose>Unspecified</pose>
+        <truncated>0</truncated>
+        <occluded>0</occluded>
+        <difficult>0</difficult>
+        <bndbox>
+            <xmin>68</xmin>
+            <ymin>136</ymin>
+            <xmax>233</xmax>
+            <ymax>253</ymax>
+        </bndbox>
+    </object>
+</annotation>

detection/TEST/00.xml

@@ -0,0 +1,86 @@
+<annotation>
+	<folder> bell pepper leaf spot </folder>
+	<filename>00.jpg</filename>
+	<path>/home/pranjali/Desktop/sem6/ML_Project/latestDataset/ bell pepper leaf spot /00.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>480</width>
+		<height>360</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>Bell_pepper leaf spot</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>88</xmin>
+			<ymin>26</ymin>
+			<xmax>419</xmax>
+			<ymax>259</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>Bell_pepper leaf spot</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>126</xmin>
+			<ymin>251</ymin>
+			<xmax>459</xmax>
+			<ymax>357</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>Bell_pepper leaf spot</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>70</ymin>
+			<xmax>134</xmax>
+			<ymax>238</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>Bell_pepper leaf spot</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>381</xmin>
+			<ymin>1</ymin>
+			<xmax>480</xmax>
+			<ymax>131</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>Bell_pepper leaf spot</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>389</xmin>
+			<ymin>99</ymin>
+			<xmax>479</xmax>
+			<ymax>224</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>Bell_pepper leaf spot</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>8</xmin>
+			<ymin>203</ymin>
+			<xmax>158</xmax>
+			<ymax>334</ymax>
+		</bndbox>
+	</object>
+</annotation>

detection/TEST/000.xml

@@ -0,0 +1,26 @@
+<annotation>
+	<folder> bell pepper leaf spot </folder>
+	<filename>000.jpg</filename>
+	<path>/home/pranjali/Desktop/sem6/ML_Project/latestDataset/ bell pepper leaf spot /000.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1024</width>
+		<height>768</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>Bell_pepper leaf spot</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>252</xmin>
+			<ymin>1</ymin>
+			<xmax>942</xmax>
+			<ymax>741</ymax>
+		</bndbox>
+	</object>
+</annotation>

detection/TEST/0000.xml

@@ -0,0 +1,26 @@
+<annotation>
+	<folder> grape leaf </folder>
+	<filename>0000.jpg</filename>
+	<path>/home/pranjali/Desktop/sem6/ML_Project/latestDataset/ grape leaf /0000.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1000</width>
+		<height>1000</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>grape leaf</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>3</xmin>
+			<ymin>27</ymin>
+			<xmax>941</xmax>
+			<ymax>939</ymax>
+		</bndbox>
+	</object>
+</annotation>

detection/TEST/00pe.xml

@@ -0,0 +1,26 @@
+<annotation>
+	<folder> peach leaf </folder>
+	<filename>00pe.jpg</filename>
+	<path>/home/pranjali/Desktop/sem6/ML_Project/latestDataset/ peach leaf /00pe.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>720</width>
+		<height>1000</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>Peach leaf</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>41</xmin>
+			<ymin>39</ymin>
+			<xmax>700</xmax>
+			<ymax>731</ymax>
+		</bndbox>
+	</object>
+</annotation>

detection/TEST/01.xml

@@ -0,0 +1,26 @@
+<annotation>
+	<folder> bell pepper leaf spot </folder>
+	<filename>01.jpg</filename>
+	<path>/home/pranjali/Desktop/sem6/ML_Project/latestDataset/ bell pepper leaf spot /01.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>638</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>Bell_pepper leaf spot</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>409</xmin>
+			<ymin>171</ymin>
+			<xmax>548</xmax>
+			<ymax>416</ymax>
+		</bndbox>
+	</object>
+</annotation>

detection/TEST/02.-Rust-2017-207u24s.xml

@@ -0,0 +1,26 @@
+<annotation>
+	<folder> apple rust leaf </folder>
+	<filename>02.-Rust-2017-207u24s.jpg</filename>
+	<path>/home/davinder/ISDf/ apple rust leaf /02.-Rust-2017-207u24s.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>5312</width>
+		<height>2988</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>Apple rust leaf</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1690</xmin>
+			<ymin>324</ymin>
+			<xmax>5078</xmax>
+			<ymax>2397</ymax>
+		</bndbox>
+	</object>
+</annotation>

detection/TEST/02c.xml

@@ -0,0 +1,26 @@
+<annotation>
+	<folder> leaf blight of corn </folder>
+	<filename>02c.jpg</filename>
+	<path>/home/pranjali/Desktop/sem6/ML_Project/latestDataset/ leaf blight of corn /02c.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>960</width>
+		<height>540</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>Corn leaf blight</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>7</xmin>
+			<ymin>62</ymin>
+			<xmax>958</xmax>
+			<ymax>465</ymax>
+		</bndbox>
+	</object>
+</annotation>

detection/TEST/039b47d574bc4bb8a14259a1cd96a741.xml

@@ -0,0 +1,26 @@
+<annotation>
+	<folder> Tomato Early blight leaf </folder>
+	<filename>039b47d574bc4bb8a14259a1cd96a741.jpg</filename>
+	<path>/home/sohamp/Desktop/done/ Tomato Early blight leaf /039b47d574bc4bb8a14259a1cd96a741.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>638</width>
+		<height>479</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>Tomato Early blight leaf</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>329</xmin>
+			<ymin>202</ymin>
+			<xmax>607</xmax>
+			<ymax>389</ymax>
+		</bndbox>
+	</object>
+</annotation>

detection/TEST/03gb.xml

@@ -0,0 +1,26 @@
+<annotation>
+	<folder> grape leaf black rot </folder>
+	<filename>03gb.jpg</filename>
+	<path>/home/pranjali/Desktop/sem6/ML_Project/latestDataset/ grape leaf black rot /03gb.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>479</width>
+		<height>302</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>grape leaf black rot</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>176</xmin>
+			<ymin>24</ymin>
+			<xmax>470</xmax>
+			<ymax>292</ymax>
+		</bndbox>
+	</object>
+</annotation>

detection/TEST/052609%20Hartman%20Crabapple%20scab%20single%20leaf.JPG.xml

@@ -0,0 +1,26 @@
+<annotation>
+	<folder> Apple Scab Leaf </folder>
+	<filename>052609%20Hartman%20Crabapple%20scab%20single%20leaf.JPG.jpg</filename>
+	<path>/home/davinder/ISDf/ Apple Scab Leaf /052609%20Hartman%20Crabapple%20scab%20single%20leaf.JPG.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>496</width>
+		<height>348</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>Apple Scab Leaf</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>41</xmin>
+			<ymin>41</ymin>
+			<xmax>464</xmax>
+			<ymax>285</ymax>
+		</bndbox>
+	</object>
+</annotation>

detection/TEST/0605_Rust-induced_leafspot.xml

@@ -0,0 +1,26 @@
+<annotation>
+	<folder> apple rust leaf </folder>
+	<filename>0605_Rust-induced_leafspot.jpg</filename>
+	<path>/home/davinder/ISDf/ apple rust leaf /0605_Rust-induced_leafspot.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>341</width>
+		<height>188</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>Apple rust leaf</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>6</xmin>
+			<ymin>10</ymin>
+			<xmax>299</xmax>
+			<ymax>182</ymax>
+		</bndbox>
+	</object>
+</annotation>

detection/TEST/07.17.18-Common_Tomato_Diseases_Canker-258x300.xml

@@ -0,0 +1,23 @@
+<annotation>
+    <folder> tomato leaf bacterial spot </folder>
+    <filename>07.17.18-Common_Tomato_Diseases_Canker-258x300.jpg</filename>
+    <size>
+        <width>258</width>
+        <height>300</height>
+        <depth>3</depth>
+    </size>
+    <segmented>0</segmented>
+    <object>
+        <name>Tomato leaf bacterial spot</name>
+        <pose>Unspecified</pose>
+        <truncated>0</truncated>
+        <occluded>0</occluded>
+        <difficult>0</difficult>
+        <bndbox>
+            <xmin>22</xmin>
+            <ymin>9</ymin>
+            <xmax>235</xmax>
+            <ymax>284</ymax>
+        </bndbox>
+    </object>
+</annotation>

detection/TEST/0796.20graylssymt.xml

@@ -0,0 +1,26 @@
+<annotation>
+	<folder> leaf blight of corn </folder>
+	<filename>0796.20graylssymt.jpg</filename>
+	<path>/home/pranjali/Desktop/sem6/ML_Project/latestDataset/ leaf blight of corn /0796.20graylssymt.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>432</width>
+		<height>350</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>Corn leaf blight</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>2</xmin>
+			<ymin>46</ymin>
+			<xmax>431</xmax>
+			<ymax>324</ymax>
+		</bndbox>
+	</object>
+</annotation>

detection/TEST/0796.39maizerust.xml

@@ -0,0 +1,26 @@
+<annotation>
+	<folder> corn rust leaf </folder>
+	<filename>0796.39maizerust.jpg</filename>
+	<path>/home/davinder/ISDf/ corn rust leaf /0796.39maizerust.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>432</width>
+		<height>287</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>Corn rust leaf</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>40</xmin>
+			<ymin>3</ymin>
+			<xmax>405</xmax>
+			<ymax>269</ymax>
+		</bndbox>
+	</object>
+</annotation>

detection/TEST/0796.40comrust.xml

@@ -0,0 +1,26 @@
+<annotation>
+	<folder> corn rust leaf </folder>
+	<filename>0796.40comrust.jpg</filename>
+	<path>/home/davinder/ISDf/ corn rust leaf /0796.40comrust.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>432</width>
+		<height>268</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>Corn rust leaf</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>15</ymin>
+			<xmax>398</xmax>
+			<ymax>268</ymax>
+		</bndbox>
+	</object>
+</annotation>

detection/TEST/0796.47southrust.xml

@@ -0,0 +1,26 @@
+<annotation>
+	<folder> corn rust leaf </folder>
+	<filename>0796.47southrust.jpg</filename>
+	<path>/home/davinder/ISDf/ corn rust leaf /0796.47southrust.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>432</width>
+		<height>252</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>Corn rust leaf</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>12</ymin>
+			<xmax>432</xmax>
+			<ymax>226</ymax>
+		</bndbox>
+	</object>
+</annotation>

detection/TEST/0796.52srusttelia.xml

@@ -0,0 +1,26 @@
+<annotation>
+	<folder> corn rust leaf </folder>
+	<filename>0796.52srusttelia.jpg</filename>
+	<path>/home/davinder/ISDf/ corn rust leaf /0796.52srusttelia.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>432</width>
+		<height>309</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>Corn rust leaf</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>12</ymin>
+			<xmax>432</xmax>
+			<ymax>291</ymax>
+		</bndbox>
+	</object>
+</annotation>

detection/TEST/07c.xml

@@ -0,0 +1,26 @@
+<annotation>
+	<folder> leaf blight of corn </folder>
+	<filename>07c.jpg</filename>
+	<path>/home/pranjali/Desktop/sem6/ML_Project/latestDataset/ leaf blight of corn /07c.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>675</width>
+		<height>414</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>Corn leaf blight</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>38</xmin>
+			<ymin>55</ymin>
+			<xmax>653</xmax>
+			<ymax>406</ymax>
+		</bndbox>
+	</object>
+</annotation>

detection/TEST/07feb_ma_sbr3.JPG.xml

@@ -0,0 +1,26 @@
+<annotation>
+	<folder> soyabean leaf </folder>
+	<filename>07feb_ma_sbr3.JPG.jpg</filename>
+	<path>/media/master/Data/Sem6/ISDl/Pratik/ soyabean leaf /07feb_ma_sbr3.JPG.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1296</width>
+		<height>864</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>Soyabean leaf</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>154</xmin>
+			<ymin>188</ymin>
+			<xmax>1049</xmax>
+			<ymax>810</ymax>
+		</bndbox>
+	</object>
+</annotation>

detection/TEST/0c.xml

@@ -0,0 +1,26 @@
+<annotation>
+	<folder> leaf blight of corn </folder>
+	<filename>0c.jpg</filename>
+	<path>/home/pranjali/Desktop/sem6/ML_Project/latestDataset/ leaf blight of corn /0c.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>1170</width>
+		<height>501</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>Corn leaf blight</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>6</xmin>
+			<ymin>4</ymin>
+			<xmax>1143</xmax>
+			<ymax>495</ymax>
+		</bndbox>
+	</object>
+</annotation>

detection/TEST/100983448.xml

@@ -0,0 +1,26 @@
+<annotation>
+	<folder> Tomato Early blight leaf </folder>
+	<filename>100983448.jpg</filename>
+	<path>/home/sohamp/Desktop/done/ Tomato Early blight leaf /100983448.jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>448</width>
+		<height>448</height>
+		<depth>3</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>Tomato Early blight leaf</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>47</xmin>
+			<ymin>25</ymin>
+			<xmax>406</xmax>
+			<ymax>433</ymax>
+		</bndbox>
+	</object>
+</annotation>