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docs/en/SUMMARY.md

@@ -103,3 +103,4 @@
   * [Copter Hack 2017](copterhack2017.md)
   * [Robocross-2019](robocross2019.md)
   * [Camera calibration (legacy)](camera_calib.md)
+  * [Recognition of crop types in agriculture](agriculture.md)

docs/en/agriculture.md

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+# Recognition of crop types in mass agricultural production
+
+## Introduction
+
+Modern agriculture in many countries is becoming one of the shining examples of the rapid and successful introduction of new technologies. Unmanned aerial vehicles are capable of performing a wide range of tasks, among which monitoring of agricultural land has now become a common tool for increasing the efficiency of agriculture. The goal of my project is to write a code for recognizing crop types in mass agricultural production. In the future, from the recognition results, you can design a map of sown areas.
+
+## Monitoring
+
+In agriculture, monitoring is necessary to obtain information on the state of land and crops. Based on the monitoring results, farmers or specialists can understand whether crops are sprouting normally, whether there is a threat from weeds and/or insects - pests, what is the degree of moisture in individual areas or entire areas, etc.
+
+## Explanation of the code
+
+Import libraries:
+
+```python
+import rospy
+import cv2
+from sensor_msgs.msg import Image
+from cv_bridge import CvBridge
+import numpy as np
+```
+
+Create some variables:
+
+```python
+rospy.init_node('computer_vision_sample')
+
+bridge = CvBridge()
+
+color = 'undefined'
+shape = 'undefined'
+culture = ""
+```
+
+To implement computer vision algorithms, it is recommended to use the OpenCV library preinstalled on the Clover image.
+Create a subscriber for the topic with the image from the main camera for processing using OpenCV:
+
+```python
+def image_colback_color(data):
+    global color, shape
+
+    cv_image = bridge.imgmsg_to_cv2(data, 'bgr8') # OpenCV image
+    img_hsv = cv2.cvtColor(cv_image, cv2.COLOR_BGR2HSV) #[118:119,158:159] 
+
+    #detected color
+    #print(img_hsv[0][0])
+```
+
+Each culture has its unique shade (wheat is golden, buckwheat is light brown).
+
+<img src="../assets/field.jpg" width="75%">
+<img src="../assets/field2.jpg" width="75%">
+
+We describe color ranges for certain crops:
+
+```python
+#wheat
+yellow_orange_low = (38, 110, 150)
+yellow_orange_high= (52, 110, 150)
+
+#buckwheat
+brown_low = (23, 50, 50)
+brown_high= (37, 50, 50)
+
+yellow_orange_mask = cv2.inRange(img_hsv, yellow_orange_low, yellow_orange_high)
+brown_mask = cv2.inRange(img_hsv, brown_low, brown_high)
+
+if yellow_orange_mask[119][159] == 255:
+    shape = shape_recog(yellow_orange_mask)
+
+elif brown_mask[119][159] == 255:
+    shape = shape_recog(brown_mask)
+
+else:
+    shape = 'undefined'
+    color = 'undefined'
+
+if shape = 'brown':
+    culture = "greshiha"
+if shape = 'yellow_orange':
+    culture = "pshenitsa"
+
+image_sub = rospy.Subscriber('main_camera/image_raw', Image, image_colback_color) 
+```
+
+The script will take up to 100% CPU capacity. To slow down the script artificially, you can use throttling of frames from the camera, for example, at 5 Hz (`main_camera.launch`):
+
+```xml
+<node pkg="topic_tools" name="cam_throttle" type="throttle" args="messages main_camera/image_raw 5.0 main_camera/image_raw_throttled"/>
+```
+
+The topic for the subscriber, in this case, should be changed for `main_camera/image_raw_throttled`.
+
+```python
+print (culture)
+while not rospy.is_shutdown():
+    print("color: {}".format(color))
+    print("shape: {}".format(shape))
+    rospy.sleep(0.2)
+```
+
+This program will recognize the culture by its shade. We can use more color ranges to improve the accuracy of the recognition so the drone can recognize more crops.
+
+Examples of color ranges for other colors:
+
+```python
+red_low1 = (0, 110, 150)
+red_high1 = (7, 255, 255)
+
+red_low2 = (172, 110, 150)
+red_high2 = (180, 255, 255)
+
+red_orange_low = (8, 110, 150)
+red_orange_high = (22, 110, 150)
+
+orange_low = (23, 110, 150)
+orange_high = (37, 110, 150)
+
+yellow_orange_low = (38, 110, 150)
+yellow_orange_high = (52, 110, 150)
+
+yellow_low = (53, 150, 150)
+yellow_high = (67, 255, 255)
+
+yellow_green_low = (68, 150, 150)
+yellow_green_high = (82, 255, 255)
+
+green_low = (83, 150, 150)
+green_high = (97, 255, 255)
+
+blue_green_low = (98, 150, 150)
+blue_green_high = (113, 255, 255)
+
+blue_low = (114, 150, 150)
+blue_high = (127, 255, 255)
+
+blue_violet_low = (128, 150, 150)
+blue_violet_high = (142, 255, 255)
+
+violet_low = (143, 150, 150)
+violet_high = (157, 255, 255)
+
+red_violet_low = (158, 150, 150)
+red_violet_hugh = (171, 255, 255) 
+```
+
+Note that there are two ranges for red because red is at the edges of the HSV color space.

docs/ru/SUMMARY.md

@@ -119,6 +119,7 @@
   * [Блочный конструктор полета](clever_blocks.md)
   * [Калибровка камеры (legacy)](camera_calib.md)
   * [Управление дроном для оценки позы человека](human_pose_estimation_drone_control.md)
+  * [Распознавание видов агрокультур](agriculture.md)
 
 ## Учебник
 

docs/ru/agriculture.md

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+# Распознавание видов агрокультур в массовом сельском производстве
+
+## Введение
+
+Современное сельское хозяйство во многих странах превращается в один из ярких примеров быстрого и успешного внедрения новых технологий. Беспилотные летательные аппараты способны выполнять широкий круг задач, среди которых мониторинг сельскохозяйственных угодий сегодня стал уже почти привычным инструментом повышения эффективности сельских хозяйств. Целью моего проекта является написание кода для распознавания видов агрокультур в массовом сельском производстве. В дальнейшем из результатов распознавания можно спроектировать карту посевных площадей.
+
+## Мониторинг
+
+В сельском хозяйстве мониторинг необходим для получения информации о состоянии угодий и посевов. Фермеры или специалисты могут по результатам мониторинга понять, нормально ли всходят культуры, есть ли угроза со стороны сорняков и/или насекомых – вредителей, какова степень увлажненности отдельных участков или целых площадей и т.д.
+
+## Объяснение кода
+
+Подключаем библиотеки:
+
+```python
+import rospy
+import cv2
+from sensor_msgs.msg import Image
+from cv_bridge import CvBridge
+import numpy as np
+```
+
+Создаём некоторые переменные:
+
+```python
+rospy.init_node('computer_vision_sample')
+
+bridge = CvBridge()
+
+color = 'undefined'
+shape = 'undefined'
+culture = ""
+```
+
+Для реализации алгоритмов компьютерного зрения рекомендуется использовать предустановленную на образ Клевера библиотеку OpenCV.
+ Создаём подписчика на топик с изображением с основной камеры для обработки с использованием OpenCV:
+
+```python
+def image_colback_color(data):
+    global color, shape
+
+    cv_image = bridge.imgmsg_to_cv2(data, 'bgr8') # OpenCV image
+    img_hsv = cv2.cvtColor(cv_image, cv2.COLOR_BGR2HSV) #[118:119,158:159]
+
+    #detected color
+    #print(img_hsv[0][0])
+```
+
+Каждая культура имеет свой неповторимый оттенок(пшеница- золотистая, гречиха - светло-коричневая).
+
+<img src="../assets/field.jpg" width="75%">
+<img src="../assets/field2.jpg" width="75%">
+
+Прописываем диапазоны цветов для определённых культур:
+
+```python
+#пшеница
+yellow_orange_low = (38, 110, 150)
+yellow_orange_high= (52, 110, 150)
+
+#гречиха
+brown_low = (23, 50, 50)
+brown_high= (37, 50, 50)
+
+yellow_orange_mask = cv2.inRange(img_hsv, yellow_orange_low, yellow_orange_high)
+brown_mask = cv2.inRange(img_hsv, brown_low, brown_high)
+
+if yellow_orange_mask[119][159] == 255:
+    shape = shape_recog(yellow_orange_mask)
+
+elif brown_mask[119][159] == 255:
+    shape = shape_recog(brown_mask)
+
+else:
+    shape = 'undefined'
+    color = 'undefined' 
+
+if shape = 'brown':
+    culture = "greshiha"
+if shape = 'yellow_orange':
+    culture = "pshenitsa"
+
+image_sub = rospy.Subscriber('main_camera/image_raw', Image, image_colback_color)
+```
+
+Скрипт будет занимать 100% процессора. Для искусственного замедления работы скрипта можно запустить throttling кадров с камеры, например, в 5 Гц (`main_camera.launch`):
+
+```xml
+<node pkg="topic_tools" name="cam_throttle" type="throttle" args="messages main_camera/image_raw 5.0 main_camera/image_raw_throttled"/>
+```
+
+Топик для подписчика в этом случае необходимо поменять на: `main_camera/image_raw_throttled`.
+
+```python
+print (culture)
+while not rospy.is_shutdown():
+    print("color: {}".format(color))
+    print("shape: {}".format(shape))
+    rospy.sleep(0.2) 
+```
+
+Данная программа будет определять культуру по её оттенку. Для повышения точности определения можно использовать больше цветовых диапазонов и дрон сможет распознавать большее колличество культур.
+
+Вот примеры цветовых диапазонов:
+
+```python
+red_low1 = (0, 110, 150)
+red_high1 = (7, 255, 255)
+
+red_low2 = (172, 110, 150)
+red_high2 = (180, 255, 255)
+
+red_orange_low = (8, 110, 150)
+red_orange_high = (22, 110, 150)
+
+orange_low = (23, 110, 150)
+orange_high = (37, 110, 150)
+
+yellow_orange_low = (38, 110, 150)
+yellow_orange_high = (52, 110, 150)
+
+yellow_low = (53, 150, 150)
+yellow_high = (67, 255, 255)
+
+yellow_green_low = (68, 150, 150)
+yellow_green_high = (82, 255, 255)
+
+green_low = (83, 150, 150)
+green_high = (97, 255, 255)
+
+blue_green_low = (98, 150, 150)
+blue_green_high = (113, 255, 255)
+
+blue_low = (114, 150, 150)
+blue_high = (127, 255, 255)
+
+blue_violet_low = (128, 150, 150)
+blue_violet_high = (142, 255, 255)
+
+violet_low = (143, 150, 150)
+violet_high = (157, 255, 255)
+
+red_violet_low = (158, 150, 150)
+red_violet_hugh = (171, 255, 255) 
+```
+
+Обратите внимание, что для красного цвета используется два диапазона т. к. красный цвет находится на границах цветового пространства HSV.