AgDrone System™

, 9–10

Agricultural machinery

, 20

Agrochemicals

, 21, 25–27

advantages of

, 25–26

disadvantages of

, 26–27

Agroptima

, 12, 14

Agrotechnology

, 81–83

Agrotechnology solutions

, 111–116

aim and objectives

, 116–117

feasibility analysis

, 157–161

proof-of-concept activities

, 118–141

Agrowin

, 11, 14

Aguascalientes

, 35

Aguasmarket

, 12, 14

Arduino Mega 2560

, 59, 67, 74–75, 76–77, 118–119, 122–124, 126–127, 129–130, 133–134, 136–140, 145–147, 155–157, 160–161, 163, 164, 166–169

hardware components

, 73

technical specification

, 78

Artificial neural networks

, xviii

ATmega2560 microcontroller

, 165–166

Atmospheric sensors, technical specification of

, 62, 63

Battery life

, 69–70

Battery power consumption calculation

, 71

BIS Research

, 58–59

Brioagrow

, 8–9, 14

Broccoli (Brassica oleracea)

plantation of

, 4–7

producers, in Mexico

, 44–47

Business user case

, 84–86

Campeche

, 35

Cargill

, 20–21

Chiapas

, 36

Coahuila

, 36

Colima

, 36

Comma-separated value (CSV)

, 119–120, 123, 125–127, 129, 131–132, 143–144

Crop menu sequence

, 99

Crop user case description

, 87–88

Data acquisition (DAQ) system

, 114–115, 117

Database (DB)

creation

, 1–3

development and implementation of

, 3–4

sequence, filling

, 99

user case description, filling

, 90, 92, 96

Data mining

, 1–3

DHT22 sensor

, 146–147, 155, 156–157

Differentiated integral processes

, xviii

Direct sowing

, 21, 24–25

advantages of

, 24

disadvantages of

, 24–25

DS3231 RTC

, 155–156

Durango

, 37

EC-05 (soil moisture sensor)

, 61–65

Electrical conductivity (EC) measurement

, 127–135

Employee administration user case description

, 92, 97

Employee management sequence

, 99

Entity-relationship diagram (ERD)

, 98, 101

5TE sensor probe

, 129–130, 131–132, 133–134, 135, 145–146, 152–153

5TM (soil temperature and moisture sensor)

, 60–61

Fuzzy logic

, xviii

Geographical information system (GIS)

, 22–23

Global positioning system (GPS)

, 22–23, 53–54, 58–59

Global radiation measurement

, 136–141

Graphical user interface (GUI)

, 164

GS3 (electrical conductivity sensor)

, 61

Guanajuato

, 37

Guerrero

, 37–38

Hardware brain

, 73–80

Arduino Mega 2560

, 76–77, 78

Raspberry Pi 3 Model B

, 74–76

XBee PRO Series 1

, 77–80

HCI

, 99–101

Hidalgo

, 38

Hierarchical control model

, 50–51

Historic data use case diagram

, 86–87

HMP-60 (humidity and temperature probe sensor) sensor

, 60, 121, 136–137

configuration values

, 123–124

HoneyComb

, 9–10, 14

Humidity measurement

, 120–127

Hybrid seeds

, 20

IEEE 802.15.4 standard

XBee PRO Series 1

, 78, 79–80

Intellectual property analysis

, 28–33

computer programs, protection of

, 30–33

Internal control models

, xx–xxi

Jalisco

, 38

LM355 sensor

, 165

MakeMap™ Processing

, 9–10

Matplotlib software

, 164, 167–169

Monsanto

, 20–21

Morelos

, 38

Nayarit

, 39

Non-linear control systems

, 49–50

Nuevo León

, 39

Oaxaca

, 39

Optimal control systems

, 49–50

Outside temperature measurement

, 120–127

Planting techniques

, 21–22

Prediction models

, xix–xx, xx, xxi–xxii

Proagro Productivo

, 34

Production control

, xix–xx, xx, xxi–xxii

Proof-of-concept activities

, 118–141

Puebla

, 39

Qualcomm

, 30–31

Querétaro

, 39

Quintana Roo

, 39–40

Raspberry Pi 3 Model B

, 67, 74–76, 118–120, 123–124, 125–127, 129, 133–134, 135, 137, 140–141, 143–145, 160–161, 163, 164, 166–169

hardware components

, 73

software

, 126–127

technical specification of

, 75

Remote control agriculture

, 22

Sagarpa

, 34–42

San Luis Potosí

, 40

Sensor nodes (SNs)

, 114, 115–116, 117

Sensors

, 22–23, 55–65

atmospheric sensors, technical specification of

, 62, 63

EC-05 (soil moisture sensor)

, 61–65

5TM (soil temperature and moisture sensor)

, 60–61

GS3 (electrical conductivity sensor)

, 61

HMP-60 (humidity and temperature probe sensor)

, 60

sequence management

, 99

soil sensors, technical specification of

, 62–65

SP-212 (solar radiation sensor)

, 60

use case description, management of

, 89–90, 95

Sinaloa

, 40

Sink nodes

, 65–67

structure of

, 65–66

Sismagro

, 11–12, 14

Smart Agriculture

, xviii

Smart Fertilizer Management Software

, 10–11, 14

Smart monitoring and control system (SMCS)

, 99–101, 111–112, 114–116

Soil

compaction

, 16, 17

erosion

, 16

moisture

, 127–129

productivity improvement techniques

, 16

sensors, technical specification of

, 62–65

temperature measurement

, 127–135

Solar panels

, 67–73

battery power consumption calculation

, 71

technical specification of

, 72

V44 batteries

, 72

Sonora

, 40

SPI-212 sensor

, 60, 137–140, 146–147

System architecture

, 49–52

hardware brain

, 73–80

Arduino Mega 2560

, 76–77, 78

components

, 73

Raspberry Pi 3 Model B

, 74–76

XBee PRO Series 1

, 77–80

wireless sensor network

, 52–73

sensors

, 55–65

sink nodes

, 65–67

solar panels

, 67–73

Tabasco

, 41

Tamaulipas

, 41

Technology readiness level (TRL)

, 115–116, 157–161

Tlaxcala

, 41

Transgenic seeds

, 20–21, 27–28

advantages of

, 27–28

disadvantages of

, 28

UAA

, 115–116

University of Derby (UoD)

, 115–116

V44 batteries

Arduino runtime from

, 72

technical specification of

, 72

VegHands

, 7–8, 14

Veracruz

, 41–42

Volumetric water content (VWC)

, 60–61

Water conservation

, 111

Wireless sensor network (WSN)

, 52–73, 84, 113–114, 115–118, 163, 164, 166

description and functions

, 85

sensors

, 55–65

EC-05 (soil moisture sensor)

, 61–65

5TM (soil temperature and moisture sensor)

, 60–61

GS3 (electrical conductivity sensor)

, 61

HMP-60 (humidity and temperature probe sensor)

, 60

SP-212 (solar radiation sensor)

, 60

structure of

, 54

system architecture of

, 55

XBee PRO Series 1

, 59, 67, 77–80, 121, 163, 164, 165, 166, 167

hardware components

, 73

Yucatán

, 42

Zacatecas

, 42

Zigbee network

, 118

Zigbee S2C modules

, 141–142, 143