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Added Readme and example server code
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‎MachineLearning Projects/Plant Disease Prediction.ipynb renamed to ‎MachineLearning Projects/Plant Disease Prediction/Plant Disease Prediction.ipynb

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‎MachineLearning Projects/Plant Disease Prediction/README.md

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# Plant-Disease-Detection-using-Deep-learning
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Image based detection of plant diseases is an essential research topic as it may prove benefits in monitoring large fields of crops, and thus detect the symptoms of diseases as soon as they appear on plant leaves.
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You can use the ipynb file to train your model and then you can use that model for prediciting diseases in plants.
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## Demo
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![output1](https://user-images.githubusercontent.com/78130964/167244940-90f492c3-afa3-4166-8f69-b96789238a32.png)
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![output2](https://user-images.githubusercontent.com/78130964/167244958-fb800f61-fd35-4350-bdb8-abe913709bf4.png)
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## Note
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Put you trained h5 model in app folder and change the label name according to your dataset.

‎MachineLearning Projects/Plant Disease Prediction/app/app.py

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from flask import Flask, render_template, request
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from requests import get #pip install flask
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app = Flask(__name__)
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import os
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import numpy as np
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import tensorflow as tf
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from tensorflow.compat.v1 import ConfigProto
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from tensorflow.compat.v1 import InteractiveSession
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config = ConfigProto()
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config.gpu_options.per_process_gpu_memory_fraction = 0.2
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config.gpu_options.allow_growth = True
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session = InteractiveSession(config=config)
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# Keras
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from tensorflow.keras.applications.resnet50 import preprocess_input
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from tensorflow.keras.models import load_model
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from tensorflow.keras.preprocessing import image
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from werkzeug.utils import secure_filename
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MODEL_PATH ='model_inception.h5'
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# Load your trained model
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model = load_model(MODEL_PATH)
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def model_predict(img_path, model):
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print(img_path)
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img = image.load_img(img_path, target_size=(224, 224))
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x = image.img_to_array(img)
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x=x/255
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x = np.expand_dims(x, axis=0)
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preds = model.predict(x)
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preds=np.argmax(preds, axis=1)
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if preds==0:
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preds="Healthy"
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elif preds==1:
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preds="Powdery"
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elif preds==2:
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preds="Rust"
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return preds
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# route
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@app.route('/', methods=["GET", "POST"])
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def index():
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# a=svd.predict(1, 302, 3)
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return render_template('index.html')
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@app.route('/analyze', methods=["GET", "POST"])
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async def analyze():
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if request.method == 'POST':
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f = request.files['imagefile']
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basepath = os.path.dirname(__file__)
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file_path = os.path.join(
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basepath, 'uploads', secure_filename(f.filename))
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f.save(file_path)
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preds = model_predict(file_path, model)
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result=preds
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return render_template('res.html', result=result)
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return render_template('res.html', result="Sorry!! we're not able to find any relevent result for your image, please check the image and retry!!")
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if __name__ == "__main__":
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app.run(debug=True)

‎MachineLearning Projects/Plant Disease Prediction/app/static/bg.gif

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‎MachineLearning Projects/Plant Disease Prediction/app/static/style.css

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‎MachineLearning Projects/Plant Disease Prediction/app/templates/index.html

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<!DOCTYPE html>
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<html lang="en">
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<head>
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<!-- Required meta tags -->
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1" />
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<!-- Bootstrap CSS -->
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<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css" rel="stylesheet"
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integrity="sha384-1BmE4kWBq78iYhFldvKuhfTAU6auU8tT94WrHftjDbrCEXSU1oBoqyl2QvZ6jIW3" crossorigin="anonymous" />
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<title>Plant Disease Prediction</title>
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<style>
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body{
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margin: 0%;
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padding: 0%;
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background-color: bisque;
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background-image: url("/static/bg.gif");
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background-repeat: repeat-x;
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}
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.container{
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font-family: "cursive";
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display: grid;
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justify-content: center;
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align-items: center;
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}
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</style>
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</head>
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<body>
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<div class="container my-5">
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<form id="analysis-form" method="post" action="analyze" enctype="multipart/form-data">
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<label class="add">SELECT IMAGE</label>
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<input name="imagefile" id="imagefile" type="file" />
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<div>
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<button class="btn btn-primary">Analyze</button>
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</div>
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</form>
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</div>
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<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/js/bootstrap.bundle.min.js"
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integrity="sha384-ka7Sk0Gln4gmtz2MlQnikT1wXgYsOg+OMhuP+IlRH9sENBO0LRn5q+8nbTov4+1p"
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crossorigin="anonymous"></script>
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</body>
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</html>

‎MachineLearning Projects/Plant Disease Prediction/app/templates/res.html

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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8" />
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<meta http-equiv="X-UA-Compatible" content="IE=edge" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>Result : Plant Disease Prediction</title>
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<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css" rel="stylesheet"
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integrity="sha384-1BmE4kWBq78iYhFldvKuhfTAU6auU8tT94WrHftjDbrCEXSU1oBoqyl2QvZ6jIW3" crossorigin="anonymous" />
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<style>
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body {
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background-color: beige;
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}
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</style>
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</head>
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<body>
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<div class="container" style="background-color: aquamarine;">
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<h2 class="text-center mt-5 py-3">Plant Disease Prediction</h2>
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<div class="container">
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<p><strong> The plant is classified as: {{result}}</strong></p>
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<p>Thanks for using our service</p>
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<a href="/">
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<button class="btn btn-outline-success mb-5" type="submit"><strong>Go Back</strong></button>
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</a>
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</div>
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</div>
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<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/js/bootstrap.bundle.min.js"
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integrity="sha384-ka7Sk0Gln4gmtz2MlQnikT1wXgYsOg+OMhuP+IlRH9sENBO0LRn5q+8nbTov4+1p"
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crossorigin="anonymous"></script>
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</body>
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</html>

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