Insect detection part

fertilizerProjBackend/.idea/fertilizerProjBackend.iml

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fertilizerProjBackend/.idea/inspectionProfiles/profiles_settings.xml

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fertilizerProjBackend/.idea/misc.xml

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fertilizerProjBackend/.idea/modules.xml

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fertilizerProjBackend/.idea/vcs.xml

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fertilizerProjBackend/.idea/workspace.xml

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fertilizerProjBackend/fertilizerProjBackend/app.py

+from flask import Flask
+
+
+# Define a flask app
+app = Flask(__name__)
+UPLOAD_FOLDER = 'uploads'
+
+app.secret_key = "secret key"
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+
+
+
+

fertilizerProjBackend/fertilizerProjBackend/index.py

+import os
+import flask
+import json
+from flask import render_template, request
+from flask import jsonify
+from werkzeug.utils import secure_filename
+from app import app
+from main import getColorPrediction, getInsectPrediction, getLeafPrediction, predictFertilizer
+
+
+@app.route('/', methods=['GET'])
+def index():
+    # Main page
+    return render_template('index.html')
+
+
+@app.route('/predictInsect', methods=['POST'])
+def submit_file():
+    if request.method == 'POST':
+        if 'file' not in request.files:
+            return jsonify({'error': 'No file part'})
+        file = request.files['file']
+        if file.filename == '':
+            return jsonify({'error': 'No file selected for uploading'})
+        if file:
+            filename = secure_filename(file.filename)
+            file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
+            label, acc = getInsectPrediction(filename)
+            response = {
+                'label': label,
+                'probability': acc
+            }
+            return jsonify(response)
+    else:
+        return jsonify({'error': 'Invalid request method'})
+
+
+@app.route('/predictColor', methods=['POST'])
+def submit_file_color():
+    if request.method == 'POST':
+        if 'file' not in request.files:
+            return jsonify({'error': 'No file part'})
+        file = request.files['file']
+        if file.filename == '':
+            return jsonify({'error': 'No file selected for uploading'})
+        if file:
+            filename = secure_filename(file.filename)
+            file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
+            label, acc = getColorPrediction(filename)
+            response = {
+                'label': label,
+                'probability': acc
+            }
+            return jsonify(response)
+    else:
+        return jsonify({'error': 'Invalid request method'})
+
+
+@app.route('/predictLeaf', methods=['POST'])
+def submit_file_leaf():
+    if request.method == 'POST':
+        if 'file' not in request.files:
+            return jsonify({'error': 'No file part'})
+        file = request.files['file']
+        if file.filename == '':
+            return jsonify({'error': 'No file selected for uploading'})
+        if file:
+            filename = secure_filename(file.filename)
+            file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
+            label, acc = getLeafPrediction(filename)
+            response = {
+                'label': label,
+                'probability': acc
+            }
+            return jsonify(response)
+    else:
+        return jsonify({'error': 'Invalid request method'})
+
+
+@app.route('/predictFertilizer', methods=['POST'])
+def submit_yield():
+    msg_received = flask.request.get_json()
+    rsp = predictFertilizer(msg_received)
+    return json.dumps(rsp.__dict__)
+
+
+if __name__ == '__main__':
+    app.run(debug=True, port=4000)

fertilizerProjBackend/fertilizerProjBackend/main.py

+import tensorflow as tf
+import numpy as np
+import pandas as pd
+from keras_preprocessing.image import load_img
+from keras_preprocessing.image import img_to_array
+from teachable_machine import TeachableMachine
+from sklearn.linear_model import LinearRegression
+from response import Response
+
+# Model Saved Path
+INSECT_MODEL_PATH = 'models/insect_type_model.h5'
+INSECT_MODEL_LABELS_PATH = 'models/insect_type_model_labels.txt'
+
+TYPE_MODEL_PATH = 'models/paddy_or_other_pest_model.h5'
+TYPE_MODEL_LABELS_PATH = 'models/paddy_or_other_pest_model_labels.txt'
+
+LEAF_MODEL_PATH = 'models/healthy_disease_leaf_model.h5'
+LEAF_MODEL_LABELS_PATH = 'models/healthy_disease_leaf_model_labels.txt'
+
+COLOR_MODEL_PATH = 'models/colors_level_model.h5'
+COLOR_MODEL_LABELS_PATH = 'models/colors_level_model_labels.txt'
+
+# Define the mapping from class indices to class labels
+type_class_labels = {0: 'PaddyPest', 1: 'OtherPest'}
+class_labels = {0: 'rice leaf roller', 1: 'rice leaf caterpillar ', 2: 'paddy stem maggot', 3: 'asiatic rice borer',
+                4: 'yellow rice borer', 5: 'rice gall midge ', 6: 'Rice Stemfly', 7: 'brown plant hopper',
+                8: 'white backed plant hopper',
+                9: 'rice leafhopper', 10: 'Paddy Bug'}
+color_labels = {0: 'ColorL2', 1: 'ColorL3', 2: 'ColorL4', 3: 'ColorL5', }
+leaf_labels = {0: 'Healthy', 1: 'BrownSpot', 2: 'Hispa', 3: 'LeafBlast', 4: 'Bacterialleafblight'}
+
+
+def getInsectPrediction(filename):
+    typeModel = TeachableMachine(model_path=TYPE_MODEL_PATH,
+                                 labels_file_path=TYPE_MODEL_LABELS_PATH)
+
+    image_path = 'uploads/' + filename
+
+    result = typeModel.classify_image(image_path)
+
+    print("class_index", result["class_index"])
+
+    print("class_name:::", result["class_name"])
+
+    print("class_confidence:", result["class_confidence"])
+
+    typeLabel = str(result["class_name"]).rstrip()
+
+    if typeLabel == 'PaddyPest':
+        insectModel = TeachableMachine(model_path=INSECT_MODEL_PATH,
+                                       labels_file_path=INSECT_MODEL_LABELS_PATH)
+
+        image_path = 'uploads/' + filename
+
+        result = insectModel.classify_image(image_path)
+
+        print("class_index", result["class_index"])
+
+        print("class_name:::", result["class_name"])
+
+        print("class_confidence:", result["class_confidence"])
+
+        insect_label = str(result["class_name"]).rstrip()
+        probability = str(result["class_confidence"])
+
+        return insect_label, probability
+    else:
+        print('Not a paddy pest')
+        return typeLabel, 0.0
+
+
+def getColorPrediction(filename):
+    colorModel = TeachableMachine(model_path=COLOR_MODEL_PATH,
+                                  labels_file_path=COLOR_MODEL_LABELS_PATH)
+
+    image_path = 'uploads/' + filename
+
+    result = colorModel.classify_image(image_path)
+
+    print("class_index", result["class_index"])
+
+    print("class_name:::", result["class_name"])
+
+    print("class_confidence:", result["class_confidence"])
+
+    colorLabel = str(result["class_name"])
+    probability = str(result["class_confidence"])
+
+    return colorLabel, probability
+
+
+def getLeafPrediction(filename):
+    model = TeachableMachine(model_path=LEAF_MODEL_PATH,
+                             labels_file_path=LEAF_MODEL_LABELS_PATH)
+
+    image_path = 'uploads/' + filename
+
+    result = model.classify_image(image_path)
+
+    print("class_index", result["class_index"])
+
+    print("class_name:::", result["class_name"])
+
+    print("class_confidence:", result["class_confidence"])
+
+    label = str(result["class_name"])
+    probability = str(result["class_confidence"])
+
+    return label, probability
+
+
+def predictFertilizer(msg_received):
+    try:
+        type = msg_received["type"]
+        year = msg_received["year"]
+        month = msg_received["month"]
+        keyWord = msg_received["keyWord"]
+    except:
+        p1 = Response(1, "Required data missing.", None)
+        return p1
+
+    type = msg_received["type"]
+    year = msg_received["year"]
+    month = msg_received["month"]
+    keyWord = msg_received["keyWord"]
+
+    data = ()
+    if type == 'month':
+        data = predictFertilizerMonthly(int(year), int(month), keyWord)
+    elif type == 'year':
+        data = predictFertilizerYearly(int(year), keyWord).to_dict()
+    else:
+        data = predictFertilizer(int(year), int(month), keyWord)
+
+    p1 = Response(0, "Success.", data)
+    return p1
+
+
+def predictFertilizerMonthly(year, predMonth, predKeyWord):
+    year = year - 1
+
+    # Read the rainfall data into a DataFrame
+    df = pd.read_csv('csvFiles/fertilizer1.csv')
+
+    # Convert the date column to datetime and set it as the index
+    df['date'] = pd.to_datetime(df['date'])
+    df.set_index('date', inplace=True)
+
+    # Create a new DataFrame that contains only the crop data
+    crop_df = df[[predKeyWord]]
+
+    # Add a column for the month of each data point
+    crop_df['month'] = crop_df.index.month
+
+    # Add a column for the year of each data point
+    crop_df['year'] = crop_df.index.year
+
+    # Create a pivot table that shows the total rainfall for each month and year
+    rainfall_pivot = crop_df.pivot_table(values=predKeyWord, index='year', columns='month', aggfunc='sum')
+
+    # Create a new DataFrame that contains only the data for the previous year
+    prev_year_df = rainfall_pivot.loc[year]
+
+    # Drop the December rainfall data (since we're trying to predict it)
+    prev_year_df.drop(predMonth, inplace=True)
+
+    # Flatten the DataFrame to create two arrays (X and y) for the linear regression model
+    X = prev_year_df.index.values.reshape(-1, 1)
+    y = prev_year_df.values.reshape(-1, 1)
+
+    # Create a linear regression model and fit it to the data
+    model = LinearRegression()
+    model.fit(X, y)
+
+    # Predict the rainfall for December of the current year
+    current_year = pd.Timestamp.now().year
+    X_pred = [[current_year]]
+    y_pred = model.predict(X_pred)
+
+    # Print the predicted rainfall for December of the current year
+    print('Our guess for this year\'s December ' + predKeyWord + ' is:', y_pred[0][0])
+
+    return y_pred[0][0]
+
+
+def predictFertilizerYearly(year, predKeyWord):
+    # Read the rainfall data into a DataFrame
+    df = pd.read_csv('csvFiles/fertilizer1.csv')
+
+    # Convert the date column to datetime and set it as the index
+    df['date'] = pd.to_datetime(df['date'])
+    df.set_index('date', inplace=True)
+
+    # Create a new DataFrame that contains only the crop data
+    crop_df = df[[predKeyWord]]
+
+    # Add a column for the month of each data point
+    crop_df['month'] = crop_df.index.month
+
+    # Add a column for the year of each data point
+    crop_df['year'] = crop_df.index.year
+
+    # Create a pivot table that shows the total rainfall for each month and year
+    rainfall_pivot = crop_df.pivot_table(values=predKeyWord, index='year', columns='month', aggfunc='sum')
+
+    # Create a new DataFrame that contains only the data for the previous year
+    prev_year_df = rainfall_pivot.loc[year - 1]
+
+    # Drop the December rainfall data (since we're using it as the target variable)
+    # prev_year_df.drop(predMonth, inplace=True)
+
+    # Create a DataFrame to store the predictions
+    predictions_df = pd.DataFrame(columns=['month', predKeyWord])
+
+    # Loop over each month and make a prediction for the next year's rainfall
+    for month in range(1, 13):
+        # Create a new DataFrame that contains only the data for the current month
+        month_df = prev_year_df[[month]]
+
+        print(month_df)
+
+        # Flatten the DataFrame to create two arrays (X and y) for the linear regression model
+        X = month_df.index.values.reshape(-1, 1)
+        y = month_df.values.reshape(-1, 1)
+
+        # Create a linear regression model and fit it to the data
+        model = LinearRegression()
+        model.fit(X, y)
+
+        # Predict the rainfall for the next year
+        next_year = year
+        X_pred = [[next_year]]
+        y_pred = model.predict(X_pred)
+
+        # Add the prediction to the predictions DataFrame
+        predictions_df = predictions_df.append({'month': month, predKeyWord: y_pred[0][0]}, ignore_index=True)
+
+    # Print the predicted rainfall for each month of the next year
+    return predictions_df

fertilizerProjBackend/fertilizerProjBackend/requirements.txt

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+oauthlib @ file:///home/conda/feedstock_root/build_artifacts/oauthlib_1666056362788/work
+onnxruntime==1.14.1
+opencv-python==4.7.0
+opencv-python-headless==4.7.0.72
+opt-einsum @ file:///home/conda/feedstock_root/build_artifacts/opt_einsum_1617859230218/work
+packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1681337016113/work
+pandas==1.5.3
+parso @ file:///opt/conda/conda-bld/parso_1641458642106/work
+pexpect @ file:///tmp/build/80754af9/pexpect_1605563209008/work
+pickleshare @ file:///tmp/build/80754af9/pickleshare_1606932040724/work
+Pillow @ file:///Users/runner/miniforge3/conda-bld/pillow_1680694547271/work
+platformdirs @ file:///home/conda/feedstock_root/build_artifacts/platformdirs_1683850015520/work
+pooch @ file:///home/conda/feedstock_root/build_artifacts/pooch_1679580333621/work
+prompt-toolkit @ file:///tmp/build/80754af9/prompt-toolkit_1633440160888/work
+protobuf==4.21.12
+ptyprocess @ file:///tmp/build/80754af9/ptyprocess_1609355006118/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl
+pure-eval @ file:///opt/conda/conda-bld/pure_eval_1646925070566/work
+pyasn1==0.4.8
+pyasn1-modules==0.2.7
+pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work
+pydantic==1.10.7
+Pygments @ file:///opt/conda/conda-bld/pygments_1644249106324/work
+PyJWT @ file:///home/conda/feedstock_root/build_artifacts/pyjwt_1683676063469/work
+PyMatting==1.1.8
+pyOpenSSL @ file:///home/conda/feedstock_root/build_artifacts/pyopenssl_1680037383858/work
+pyparsing @ file:///home/conda/feedstock_root/build_artifacts/pyparsing_1652235407899/work
+PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1661604839144/work
+python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work
+python-multipart==0.0.6
+pytz @ file:///private/var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_c56ru3yml9/croot/pytz_1671697451306/work
+pyu2f @ file:///home/conda/feedstock_root/build_artifacts/pyu2f_1604248910016/work
+PyWavelets==1.4.1
+pyzmq @ file:///Users/ktietz/ci_310/pyzmq_1643964387809/work
+rembg==2.0.36
+requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1682535435083/work
+requests-oauthlib @ file:///home/conda/feedstock_root/build_artifacts/requests-oauthlib_1643557462909/work
+rsa @ file:///home/conda/feedstock_root/build_artifacts/rsa_1658328885051/work
+scikit-image==0.20.0
+scikit-learn @ file:///Users/ktietz/ci_310/scikit-learn_1644264513665/work
+scipy @ file:///Users/runner/miniforge3/conda-bld/scipy-split_1683900561684/work/base/dist/scipy-1.10.1-cp310-cp310-macosx_11_0_arm64.whl#sha256=a3c0cd42a1c134ecde8ed82f601d7bcc58f1bd7fe5f748f9322af7396f50d220
+seaborn==0.12.2
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+stack-data @ file:///opt/conda/conda-bld/stack_data_1646927590127/work
+starlette==0.26.1
+sympy==1.12
+tensorboard @ file:///Users/rkeith/miniconda3/conda-bld/tensorboard_1678488689416/work/tensorboard-2.11.0-py3-none-any.whl
+tensorboard-data-server @ file:///var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_91lm1y0ip6/croot/tensorboard-data-server_1670853587549/work/tensorboard_data_server-0.6.1-py3-none-macosx_11_0_arm64.whl
+tensorboard-plugin-wit @ file:///home/conda/feedstock_root/build_artifacts/tensorboard-plugin-wit_1641458951060/work/tensorboard_plugin_wit-1.8.1-py3-none-any.whl
+tensorflow @ file:///Users/mark/git/feedstocks/tensorflow-feedstock/miniforge3/conda-bld/tensorflow-split_1679779258596/work/tensorflow_pkg/tensorflow-2.11.1-cp310-cp310-macosx_11_0_arm64.whl
+tensorflow-estimator @ file:///Users/mark/git/feedstocks/tensorflow-feedstock/miniforge3/conda-bld/tensorflow-split_1679779258596/work/tensorflow-estimator/wheel_dir/tensorflow_estimator-2.11.0-py2.py3-none-any.whl
+termcolor @ file:///home/conda/feedstock_root/build_artifacts/termcolor_1682317048417/work
+threadpoolctl @ file:///Users/ktietz/demo/mc3/conda-bld/threadpoolctl_1629802263681/work
+tifffile==2023.4.12
+tornado @ file:///Users/ktietz/ci_310/tornado_1643969120498/work
+tqdm==4.65.0
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+typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/typing_extensions_1678559861143/work
+unicodedata2 @ file:///Users/runner/miniforge3/conda-bld/unicodedata2_1667239979860/work
+urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1678635778344/work
+uvicorn==0.22.0
+watchdog==3.0.0
+wcwidth @ file:///Users/ktietz/demo/mc3/conda-bld/wcwidth_1629357192024/work
+Werkzeug @ file:///home/conda/feedstock_root/build_artifacts/werkzeug_1683636301408/work
+wrapt @ file:///Users/runner/miniforge3/conda-bld/wrapt_1677485667378/work
+yarl @ file:///Users/runner/miniforge3/conda-bld/yarl_1682426738484/work
+zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1677313463193/work

fertilizerProjBackend/fertilizerProjBackend/response.py

+class Response:
+    def __init__(self, code, message, data):
+        self.code = code
+        self.message = message
+        self.data = data

fertilizerProjBackend/fertilizerProjBackend/teachable_machine.py

+from keras.models import load_model
+from PIL import Image, ImageOps
+import numpy as np
+
+
+class TeachableMachine(object):
+    '''
+    Create your TeachableMachine object to run your trained AI models.
+    '''
+
+    def __init__(self, model_path='keras_model.h5', labels_file_path='labels.txt', model_type='h5') -> None:
+        self._model_type = model_type.lower()
+        self._labels_file_path = labels_file_path
+        self._supported_types = ('keras', 'Keras', 'h5', 'h5py')
+
+        np.set_printoptions(suppress=True)
+        try:
+            self._model = load_model(model_path, compile=False)
+        except IOError as e:
+            print('LoadingModelError: Error while loading Teachable Machine model')
+            raise IOError(e)
+        except:
+            print("LoadingModelError: Error while loading Teachable Machine model")
+            raise FileNotFoundError
+        try:
+            self._labels_file = open(self._labels_file_path, "r").readlines()
+        except IOError as e:
+            print('LoadingLabelsError: Error while loading labels.txt file')
+            raise IOError(e)
+        except:
+            print("LoadingLabelsError: Error while loading labels.txt file")
+            raise FileNotFoundError
+
+        self._object_creation_status = self._model_type in self._supported_types
+        if self._object_creation_status:
+            print('Teachable Machine Object is created successfully.')
+        else:
+            raise 'NotSupportedType: Your model type is not supported, try to use types such as "keras" or "h5".'
+
+    def classify_image(self, frame_path: str):
+        '''To deploy your Teachable Machine Model on a computer/PC
+            with .h5 extension using TensorFlow.
+
+            Parameters:
+            * (str) frame_path: Provide path of the image to be classified.
+
+            Returns:
+            * class_name: Name of the highest predicted class according to labels.txt file
+            * class_index: Index or ID of the highest predicted class according to labels.txt file
+            * predictions: All prediction values for all classes.
+        '''
+        try:
+            frame = Image.open(frame_path)
+            if frame.mode != "RGB":
+                frame = frame.convert("RGB")
+        except FileNotFoundError as e:
+            print("ImageNotFound: Error in image file.")
+            raise FileNotFoundError(e)
+        except TypeError as e:
+            print(
+                "ImageTypeError: Error while converting image to RGB format, image type is not supported")
+        try:
+            if self._object_creation_status:
+                return self._get_image_classification(frame)
+        except BaseException as e:
+            print('Error in classification process, retrain your model.')
+            raise e
+
+    def _get_image_classification(self, image):
+        data = self._form_image(image)
+        prediction = self._model.predict(data)
+        class_index = np.argmax(prediction)
+        class_name = self._labels_file[class_index]
+        class_confidence = prediction[0][class_index]
+
+        return {
+            "class_name": class_name[2:],
+            "highest_class_name": class_name[2:],
+            "highest_class_id": class_index,
+            "class_index": class_index,
+            "class_id": class_index,
+            "predictions": prediction,
+            "all_predictions": prediction,
+            "class_confidence": class_confidence,
+            "highest_class_confidence": class_confidence,
+        }
+
+    def _form_image(self, image):
+        image_data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+        crop_size = (224, 224)
+        image = ImageOps.fit(image, crop_size, Image.Resampling.LANCZOS)
+        image_array = np.asarray(image)
+
+        normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+
+        image_data[0] = normalized_image_array
+        return image_data

fertilizer_recommendation_app @ 6ab1067a

+Subproject commit 6ab1067a166b8de8b41ec98432f6b257192c5ae8