Gabriel Alejandro Giraldo Santiago
Published © GPL3+

IA Agricultural monitoring system

Agricultural monitoring system with AI for the automation of processes, capacity for prevention and detection of pests and diseases.

ExpertFull instructions providedOver 12 days947
IA Agricultural monitoring system

Things used in this project

Story

Read more

Custom parts and enclosures

KV260 CASE

PRINT 3D

Schematics

fin_tmv0omtgrm_8aMByEhTkD.jpeg

Code

Model Train

Python
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 20 00:56:31 2022


"""
#%%
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.keras.callbacks import TensorBoard
from tensorflow.keras.preprocessing.image import ImageDataGenerator

#%%

#Descargamos el set de datos:
datos, metadatos = tfds.load('plant_village', as_supervised = True, with_info = True)

#%%
# Redimensionamos las imagenes:
import matplotlib.pyplot as plt
import cv2

plt.figure(figsize=(20,20))
tamaño = 80
for i, (imagen, etiqueta) in enumerate(datos['train'].take(25)):
  imagen = cv2.resize(imagen.numpy(), (tamaño, tamaño))

   
  plt.subplot(5, 5, i+1)
  
#%%
  
train_data = []

for i, (imagen, etiqueta) in enumerate(datos['train']):
  imagen = cv2.resize(imagen.numpy(), (tamaño, tamaño))
  imagen = imagen.reshape(tamaño, tamaño, 3) 
  train_data.append([imagen, etiqueta])


#%%
  
#Preparar mis variables X (entradas) y y (etiquetas) separadas

X_data = [] #imagenes de entrada (pixeles)
y_data = [] #etiquetas

for imagen, etiqueta in train_data:
  X_data.append(imagen)
  y_data.append(etiqueta)
  
#%%
  
import numpy as np

X_data = np.array(X_data).astype(float) / 255
y_data = np.array(y_data)

#%%

#Crear los modelos iniciales

modeloCNN = tf.keras.models.Sequential([
  tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(50, 50, 3)),
  tf.keras.layers.MaxPooling2D(2, 2),
  tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
  tf.keras.layers.MaxPooling2D(2, 2),
  tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
  tf.keras.layers.MaxPooling2D(2, 2),

  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(100, activation='relu'),
  tf.keras.layers.Dense(38, activation='softmax')
])

#%%

modeloCNN.compile(optimizer='adam',
                    loss='sparse_categorical_crossentropy',
                    metrics=['accuracy'])

#%%

#%%
tensorboardCNN = TensorBoard(log_dir='logs/cnn')
historyCNN = modeloCNN.fit(X_data, y_data, batch_size=32,
                           validation_split=0.15,
                           epochs=15,
                           callbacks=[tensorboardCNN])

#%%
from tensorflow.keras.preprocessing.image import ImageDataGenerator

datagen = ImageDataGenerator(
    rotation_range=30,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=15,
    zoom_range=[0.7, 1.4],
    horizontal_flip=True,
    vertical_flip=True
)

datagen.fit(X_data)

plt.figure(figsize=(20,8))

for imagen, etiqueta in datagen.flow(X_data, y_data, batch_size=10, shuffle=False):
  for i in range(10):
    plt.subplot(2, 5, i+1)
    plt.xticks([])
    plt.yticks([])
    plt.imshow(imagen[i].reshape(50, 50, 3))
  break

#%%


modeloCNN_AD = tf.keras.models.Sequential([
  tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(50, 50, 3)),
  tf.keras.layers.MaxPooling2D(2, 2),
  tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
  tf.keras.layers.MaxPooling2D(2, 2),
  tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
  tf.keras.layers.MaxPooling2D(2, 2),

  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(100, activation='relu'),
  tf.keras.layers.Dense(38, activation='softmax')
])
 
#%%

X_train = X_data[:37000]
X_valid = X_data[37000:]

y_train = y_data[:37000]
y_valid = y_data[37000:]

#%%

modeloCNN_AD.compile(optimizer='adam',
                     loss='sparse_categorical_crossentropy',
                     metrics=['accuracy'])
#%%

data_gen_train = datagen.flow(X_train, y_train, batch_size=32)


tensorboardCNN_AD = TensorBoard(log_dir='logs-new/cnn_AD')

historyCNN_AD = modeloCNN_AD.fit(
    data_gen_train,
    epochs=80, batch_size=32,
    validation_data=(X_valid, y_valid),
    steps_per_epoch=int(np.ceil(len(X_train) / float(32))),
    validation_steps=int(np.ceil(len(X_valid) / float(32))),
    callbacks=[tensorboardCNN_AD]
)


#%%

modeloCNN_AD.save('plant-model-cnn-ad.h5')

Code Quantization

Python
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 21 10:45:23 2022

XILINX CONTEST
"""


import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow import keras
from tensorflow.keras.callbacks import TensorBoard
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow_model_optimization.quantization.keras import vitis_quantize

#%%

datos, metadatos = tfds.load('plant_village', as_supervised = True, with_info = True)

#%%
import matplotlib.pyplot as plt
import cv2

plt.figure(figsize=(20,20))
tamao = 50
for i, (imagen, etiqueta) in enumerate(datos['train'].take(25)):
  imagen = cv2.resize(imagen.numpy(), (tamao, tamao))
  plt.subplot(5, 5, i+1)
  plt.imshow(imagen)
  
#%%

train_data = []

for i, (imagen, etiqueta) in enumerate(datos['train']):
  imagen = cv2.resize(imagen.numpy(), (tamao, tamao))
  imagen = imagen.reshape(tamao, tamao, 3) 
  train_data.append([imagen, etiqueta])

X_data = [] #imagenes de entrada (pixeles)
y_data = [] #etiquetas

for imagen, etiqueta in train_data:
  X_data.append(imagen)
  y_data.append(etiqueta)

import numpy as np

X_data = np.array(X_data).astype(float) / 255
y_data = np.array(y_data)

#%%
def quantize(train_generator, model):
    
    # run quantization
    quantizer = vitis_quantize.VitisQuantizer(model)
    #quantizer = tfmot.quantization.keras.quantize_model(model)
    quantized_model = quantizer.quantize_model(calib_dataset=train_generator, calib_batch_size=10)
    
    # save quantized model
    quantized_model.save('quantized_model.h5')
    return (quantized_model)

#%%
X_train = X_data[:37000]
X_valid = X_data[37000:]

y_train = y_data[:37000]
y_valid = y_data[37000:]
  
model = keras.models.load_model('my_model-cnn-ad.h5')
quantized_model = quantize(X_train, model)

compile.sh

SH
#!/bin/sh

# Copyright 2020 Xilinx Inc.
# 
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# 
#     http://www.apache.org/licenses/LICENSE-2.0
# 
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Author: Mark Harvey, Xilinx Inc

if [ $1 = zcu102 ]; then
      ARCH=/opt/vitis_ai/compiler/arch/DPUCZDX8G/ZCU102/arch.json
      TARGET=zcu102
      echo "-----------------------------------------"
      echo "COMPILING MODEL FOR ZCU102.."
      echo "-----------------------------------------"
elif [ $1 = kv260 ]; then
      ARCH = /opt/vitis_ai/compiler/arch/DPUCZDX8G/KV260/arch.json
      TARGET=kv260
      echo "-----------------------------------------"
      echo "COMPILING MODEL FOR KV260.."
      echo "-----------------------------------------"
elif [ $1 = zcu104 ]; then
      ARCH=/opt/vitis_ai/compiler/arch/DPUCZDX8G/ZCU104/arch.json
      TARGET=zcu104
      echo "-----------------------------------------"
      echo "COMPILING MODEL FOR ZCU104.."
      echo "-----------------------------------------"
elif [ $1 = vck190 ]; then
      ARCH=/opt/vitis_ai/compiler/arch/DPUCVDX8G/VCK190/arch.json
      TARGET=vck190
      echo "-----------------------------------------"
      echo "COMPILING MODEL FOR VCK190.."
      echo "-----------------------------------------"
elif [ $1 = u50 ]; then
      ARCH=/opt/vitis_ai/compiler/arch/DPUCAHX8H/U50/arch.json
      TARGET=u50
      echo "-----------------------------------------"
      echo "COMPILING MODEL FOR ALVEO U50.."
      echo "-----------------------------------------"
else
      echo  "Target not found. Valid choices are: zcu102, zcu104, vck190, u50 ..exiting"
      exit 1
fi

compile() {
      vai_c_tensorflow2 \
            --model           quantized_model.h5 \
            --arch            $ARCH \
            --output_dir      build/compiled_$TARGET \
            --net_name        customcnn
}


compile 2>&1 | tee build/logs/compile_$TARGET.log


echo "-----------------------------------------"
echo "MODEL COMPILED"
echo "-----------------------------------------"

target.py

Python
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 16 13:43:34 2022


"""
#%%
import argparse
import os
import shutil
import sys
import cv2
from tqdm import tqdm

#%%

def make_target(target_dir, image_dir, num_images, input_width, input_height, app_dir, model):
    # remove any previous data
    shutil.rmtree(target_dir, ignore_errors=True)    
    os.makedirs(target_dir)
    os.makedirs(target_dir+'/images')   
    
    # make a list of images
    images = os.listdir(image_dir)
    
    # resize each image and save to target folder
    print('Resizing and copying',num_images,'images...')
    for i in tqdm(range(num_images)):
        image = cv2.imread(os.path.join(image_dir, images[i]))
        image = cv2.resize(image, (input_width, input_height),interpolation=cv2.INTER_CUBIC)
        cv2.imwrite(os.path.join(target_dir,'images',images[i]), image)
    
    # copy application code
    print('Copying application code from',app_dir,'...')
    shutil.copy(os.path.join(app_dir, 'app_mt.py'), target_dir)

    # copy compiled model
    print('Copying compiled model from',model,'...')
    shutil.copy(model, target_dir)
    
    return
 #%%
 
 

target_dir = 'build/target'

image_dir = 'Dataset/Combinadas'

num_images = 3000

input_width = 50

input_height = 50

app_dir = 'application'

model = 'customcnn.xmodel'





make_target(target_dir, image_dir, num_images, input_width, input_height, app_dir, model)

meta.json

JSON
{
    "lib": "libvart-dpu-runner.so",
    "filename": "customcnn.xmodel",
    "fingerprint":"0x1000020F6014406",
    "kernel": [
        "subgraph_quant_conv2d_6"
    ],
    "target": "DPUCZDX8G_ISA0_B4096_MAX_BG2",

}

Temp & Hum Sensor Grove Pynq

Python
#   Copyright (c) 2016, NECST Laboratory, Politecnico di Milano
#   All rights reserved.
# 
#   Redistribution and use in source and binary forms, with or without 
#   modification, are permitted provided that the following conditions are met:
#
#   1.  Redistributions of source code must retain the above copyright notice, 
#       this list of conditions and the following disclaimer.
#
#   2.  Redistributions in binary form must reproduce the above copyright 
#       notice, this list of conditions and the following disclaimer in the 
#       documentation and/or other materials provided with the distribution.
#
#   3.  Neither the name of the copyright holder nor the names of its 
#       contributors may be used to endorse or promote products derived from 
#       this software without specific prior written permission.
#
#   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
#   AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, 
#   THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 
#   PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR 
#   CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 
#   EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 
#   PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
#   OR BUSINESS INTERRUPTION). HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, 
#   WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR 
#   OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF 
#   ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


from . import Pmod
from . import PMOD_GROVE_G3
from . import PMOD_GROVE_G4
from . import MAILBOX_OFFSET


__author__ = "Lorenzo Di Tucci, Marco Rabozzi, Giuseppe Natale"
__copyright__ = "Copyright 2016, NECST Laboratory, Politecnico di Milano"


PMOD_GROVE_TH02_PROGRAM = "pmod_grove_th02.bin"
GROVE_TH02_LOG_START = MAILBOX_OFFSET+16
GROVE_TH02_LOG_END = GROVE_TH02_LOG_START+(500*8)
CONFIG_IOP_SWITCH = 0x1
READ_DATA = 0x2
READ_AND_LOG_DATA = 0x3
STOP_LOG = 0xC


[docs]class Grove_TH02(object):
    """This class controls the Grove I2C Temperature and Humidity sensor. 
    
    Temperature & humidity sensor (high-accuracy & mini).
    Hardware version: v1.0.
    
    Attributes
    ----------
    microblaze : Pmod
        Microblaze processor instance used by this module.
    log_running : int
        The state of the log (0: stopped, 1: started).
    log_interval_ms : int
        Time in milliseconds between sampled reads.
        
    """
    def __init__(self, mb_info, gr_pin):
        """Return a new instance of an Grove_TH02 object. 
                
        Parameters
        ----------
        mb_info : dict
            A dictionary storing Microblaze information, such as the
            IP name and the reset name.
        gr_pin: list
            A group of pins on pmod-grove adapter.

        """
        if gr_pin not in [PMOD_GROVE_G3,
                          PMOD_GROVE_G4]:
            raise ValueError("Group number can only be G3 - G4.")

        self.microblaze = Pmod(mb_info, PMOD_GROVE_TH02_PROGRAM)
        self.log_interval_ms = 1000
        self.log_running = 0

        self.microblaze.write_mailbox(0, gr_pin)
        self.microblaze.write_blocking_command(CONFIG_IOP_SWITCH)

[docs]    def read(self):
        """Read the temperature and humidity values from the TH02 peripheral.
        
        Returns
        -------
        tuple
            Tuple containing (temperature, humidity)
        
        """
        self.microblaze.write_blocking_command(READ_DATA)
        [tmp, humidity] = self.microblaze.read_mailbox(0, 2)
        tmp = tmp/32 - 50
        humidity = humidity/16 - 24
        return tmp, humidity


[docs]    def start_log(self, log_interval_ms=100):
        """Start recording multiple heart rate values in a log.
        
        This method will first call set the log interval before sending
        the command.
        
        Parameters
        ----------
        log_interval_ms : int
            The time between two samples in milliseconds.
            
        Returns
        -------
        None
        
        """
        if log_interval_ms < 0:
            raise ValueError("Time between samples cannot be less than zero.")

        self.log_running = 1
        self.log_interval_ms = log_interval_ms
        self.microblaze.write_mailbox(0x4, log_interval_ms)
        self.microblaze.write_non_blocking_command(READ_AND_LOG_DATA)


[docs]    def stop_log(self):
        """Stop recording the values in the log.
        
        Simply send the command 0xC to stop the log.
            
        Returns
        -------
        None
        
        """
        if self.log_running == 1:
            self.microblaze.write_non_blocking_command(STOP_LOG)
            self.log_running = 0
        else:
            raise RuntimeError("No grove TH02 log running.")


[docs]    def get_log(self):
        """Return list of logged samples.
            
        Returns
        -------
        list
            List of tuples containing (temperature, humidity)
        
        """
        # stop logging
        self.stop_log()

        # Prep iterators and results list
        [head_ptr, tail_ptr] = self.microblaze.read_mailbox(0x8, 2)
        readings = list()

        # sweep circular buffer for samples
        if head_ptr == tail_ptr:
            return None
        elif head_ptr < tail_ptr:
            for i in range(head_ptr, tail_ptr, 8):
                [temp, humid] = self.microblaze.read_mailbox(i, 2)
                temp = temp/32 - 50
                humid = humid/16 - 24
                readings.append((temp, humid))
        else:
            for i in range(head_ptr, GROVE_TH02_LOG_END, 8):
                [temp, humid] = self.microblaze.read_mailbox(i, 2)
                temp = temp/32 - 50
                humid = humid/16 - 24
                readings.append((temp, humid))
            for i in range(GROVE_TH02_LOG_END, tail_ptr, 8):
                [temp, humid] = self.microblaze.read_mailbox(i, 2)
                temp = temp/32 - 50
                humid = humid/16 - 24
                readings.append((temp, humid))
        return readings

arch.json

JSON
{
    "fingerprint":"0x1000020F6014406"
}

preprocess.json

JSON
  "xclbin-location":"/lib/firmware/xilinx/kv260-smartcam/kv260-smartcam.xclbin",
  "ivas-library-repo": "/opt/xilinx/lib",
  "kernels": [
    {
      "kernel-name": "pp_pipeline_accel:pp_pipeline_accel_1",
      "library-name": "libivas_xpp.so",
      "config": {
        "debug_level" : 1,
        "mean_r": 104,
        "mean_g": 117,
        "mean_b": 123,
        "scale_r": 1,
        "scale_g": 1,
        "scale_b": 1
      }
    }
  ]
}

drawresult.json

JSON
{
  "xclbin-location":"/usr/lib/dpu.xclbin",
  "ivas-library-repo": "/opt/xilinx/lib",
  "element-mode":"inplace",
  "kernels" :[
    {
      "library-name":"libivas_airender.so",
      "config": {
          "fps_interval" : 10,
          "font_size" : 2,
          "font" : 1,
          "thickness" : 2,
          "debug_level" : 0,
          "label_color" : { "blue" : 0, "green" : 0, "red" : 255 },
          "label_filter" : [ "class", "probability" ],
          "classes" : [
                  {
                  "name" : "Apple___Apple_scab",
                  "blue" : 255,
                  "green" : 0,
                  "red" : 0
                  },
                  {
                  "name" : "Apple___Black_rot",
                  "blue" : 0,
                  "green" : 255,
                  "red" : 0
                  },
                  {
                  "name" : "Apple___Cedar_apple_rust"
                  "blue" : 0
                  "green" : 0,
                  "red" : 255
                  }]
      }
    }
  ]
}

aiiference.json

JSON
{
  "xclbin-location":"/lib/firmware/xilinx/kv260-smartcam/kv260-smartcam.xclbin",
  "ivas-library-repo": "/usr/lib/",
  "element-mode":"inplace",
  "kernels" :[
    {
      "library-name":"libivas_xdpuinfer.so",
      "config": {
        "model-name" : "compiled_$TARGET",
        "model-class" : "SSD",
        "model-path" : "/opt/xilinx/share/vitis_ai_library/models/kv260-smartcam",
        "run_time_model" : false,
        "need_preprocess" : false,
        "performance_test" : false,
        "debug_level" : 0
      }
    }
  ]
}

label.json

JSON
{
    "model-name":"ssd_mobilenet_v2",
    "num-labels": 38,
    "labels": [
        {
            "label": 0,
            "name": "Apple___Apple_scab",
            "display_name": "Apple___Apple_scab"
        },
        {
            "label": 1,
            "name": "Apple___Black_rot",
            "display_name": "Apple___Black_rot"
        },
        {
            "label": 2,
            "name": "Apple___Cedar_apple_rust",
            "display_name": "Apple___Cedar_apple_rust"
        },
        {
            "label": 3,
            "name": "Apple___healthy",
            "display_name": "Apple___healthy"
        },
        {
            "label": 4,
            "name": "Blueberry___healthy",
            "display_name": "Blueberry___healthy"
        },
        {
            "label": 5,
            "name": "Cherry___healthy",
            "display_name": "Cherry___healthy"
        },
        {
            "label": 6,
            "name": "Cherry___Powdery_mildew",
            "display_name": "Cherry___Powdery_mildew"
        },
        {
            "label": 7,
            "name": "Corn___Cercospora_leaf_spot Gray_leaf_spot",
            "display_name": "Corn___Cercospora_leaf_spot Gray_leaf_spot"
        },
        {
            "label": 8,
            "name": "Corn___Common_rust",
            "display_name": "Corn___Common_rust"
        },
        {
            "label": 9,
            "name": "Corn___healthy",
            "display_name": "Corn___healthy"
        },
        {
            "label": 10,
            "name": "Corn___Northern_Leaf_Blight",
            "display_name": "Corn___Northern_Leaf_Blight"
        },
        {
            "label": 11,
            "name": "Grape___Black_rot",
            "display_name": "Grape___Black_rot"
        },
        {
            "label": 12,
            "name": "Grape___Esca_(Black_Measles)",
            "display_name": "Grape___Esca_(Black_Measles)"
        },
        {
            "label": 13,
            "name": "Grape___healthy",
            "display_name": "Grape___healthy"
        },
        {
            "label": 14,
            "name": "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)",
            "display_name": "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)"
        },
        {
            "label": 15,
            "name": "Orange___Haunglongbing_(Citrus_greening)",
            "display_name": "Orange___Haunglongbing_(Citrus_greening)"
        },
        {
            "label": 16,
            "name": "Peach___Bacterial_spot",
            "display_name": "Peach___Bacterial_spot"
        },
        {
            "label": 17,
            "name": "Peach___healthy",
            "display_name": "Peach___healthy"
        },
        {
            "label": 18,
            "name": "Pepper,_bell___Bacterial_spot",
            "display_name": "Pepper,_bell___Bacterial_spot"
        },
        {
            "label": 19,
            "name": "Pepper,_bell___healthy",
            "display_name": "Pepper,_bell___healthy"
        },
        {
            "label": 20,
            "name": "Potato___Early_blight",
            "display_name": "Potato___Early_blight"
        },
        {
            "label": 21,
            "name": "Potato___healthy",
            "display_name": "Potato___healthy"
        },
        {
            "label": 22,
            "name": "Potato___Late_blight",
            "display_name": "Potato___Late_blight"
        },
        {
            "label": 23,
            "name": "Raspberry___healthy",
            "display_name": "Raspberry___healthy"
        },
        {
            "label": 24,
            "name": "Soybean___healthy",
            "display_name": "Soybean___healthy"
        },
        {
            "label": 25,
            "name": "Squash___Powdery_mildew",
            "display_name": "Squash___Powdery_mildew"
        },
        {
            "label": 26,
            "name": "Strawberry___healthy",
            "display_name": "Strawberry___healthy"
        },
        {
            "label": 27,
            "name": "Strawberry___Leaf_scorch",
            "display_name": "Strawberry___Leaf_scorch"
        },
        {
            "label": 28,
            "name": "Tomato___Bacterial_spot",
            "display_name": "Tomato___Bacterial_spot"
        },
        {
            "label": 29,
            "name": "Tomato___Early_blight",
            "display_name": "Tomato___Early_blight"
        },
        {
            "label": 30,
            "name": "Tomato___healthy",
            "display_name": "Tomato___healthy"
        },
        {
            "label": 31,
            "name": "Tomato___Late_blight",
            "display_name": "Tomato___Late_blight"
        },
        {
            "label": 32,
            "name": "Tomato___Leaf_Mold",
            "display_name": "Tomato___Leaf_Mold"
        },
        {
            "label": 33,
            "name": "Tomato___Septoria_leaf_spot",
            "display_name": "Tomato___Septoria_leaf_spot"
        },
        {
            "label": 34,
            "name": "Tomato___Spider_mites Two-spotted_spider_mite",
            "display_name": "Tomato___Spider_mites Two-spotted_spider_mite"
        },
        {
            "label": 35,
            "name": "Tomato___Target_Spot",
            "display_name": "Tomato___Target_Spot"
        },
        {
            "label": 36,
            "name": "Tomato___Tomato_mosaic_virus",
            "display_name": "Tomato___Tomato_mosaic_virus"
        },
        {
            "label": 37,
            "name": "Tomato___Tomato_Yellow_Leaf_Curl_Virus",
            "display_name": "Tomato___Tomato_Yellow_Leaf_Curl_Virus"
        }

    ]
}

VITIS AI

Credits

Gabriel Alejandro Giraldo Santiago

Gabriel Alejandro Giraldo Santiago

12 projects • 85 followers
Expert maker with strong soft skills, a leading Hispanic figure in emerging technologies, always seeking new challenges and achievements.
Thanks to Cristian Ferney Caicedo.

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