3 research outputs found
INTERNET OF THINGS IN SMART AGRICULTURE: APPLICATIONS AND OPEN CHALLENGES
Purpose of Study: The IoT is an emerging field nowadays and that can be used anywhere in automation, agriculture, controlling as well as monitoring of any object, which exists in the real world. We have to make use of IoT in Agriculture to increase productivity. Agro-industry processes could be more efficient by using IoT. It gives automation to agro-industry by reducing human intervention. In the current scenario, the sometime farmer doesnβt know the current status of the soil moisture and other things related to their land and donβt produce productive results towards crops. The purpose of this research study is to explore the usage of IoT devices and application areas that are being used in agriculture.
Methodology: The methodology behind this study is to identify trends and review the open challenges, application areas and architectures for IoT in agro-industry. This survey is based on a systematic literature review where related research is grouped into four domains such as monitoring, control, prediction, and logistics.
Main Findings: This research study presents a detailed work of the eminent researchers and designs of computer architecture that can be applied in agriculture for smart farming. This research study also highlights various unfolded challenges of IoT in agriculture.
Implications: This study can be beneficial for farmers, researchers, and professionals working in agricultural institutions for smart farming.
Novelty/Originality of the study: Various eminent researchers have been making efforts for smart farming by using IoT concepts in agriculture. But, a bouquet of unfolded challenges is still in a queue for their effective solution. This study makes some efforts to discuss past research and open challenges in IoT based agriculture
A Study on Data Analysis and Electronic Application for the Growth of Smart Farming
This paper proposed the system development especially for watering the agricultural crops depend upon the WSN. This paper focused to develop and model a control process by joint radars in the agricultural crop along with information management through web and smartphone application. The 3 elements are application of mobile, web and hardware. The first element i.e. hardware was executed and designed in manage box hardware linked to gather information about the crops. Soil humidity radars are used to detect the agricultural field linked to the control box. The 2nd element i.e. web method was web depend method which was executed and modeled to handle the details of field and crop information. This element applied information mining to examine the information for finding perfect soil humidity, moisture level and temperature. The last element i.e. mobile method was used mainly to manage field watering by a mobile method in a phone. This allows manual or automatic control by the controller. An automatic control uses information from soil humidity radars for watering the crops. The user may choose the manual method for watering the field in the system control method. The method may send notifications by LINE API for the line app. The method was tested and executed in Northeast India. The outputs displayed the executions to be helpful in the field of agriculture. The humidity level of the soil was appropriately maintained for improving manufacturing in agriculture, growth of vegetables and decreasing cost. Therefore, this paper displays the driving agriculture field by digital creativity
Time of chemical treatments prediction in agricultural production based on Data mining techniques using wireless communication systems
Π£ΡΠΏΠ΅ΡΠ½ΠΎ ΠΎΠ΄ΡΠ΅ΡΠΈΠ²Π°ΡΠ΅ Π²ΡΠ΅ΠΌΠ΅Π½ΡΠΊΠΎΠ³ ΠΏΠ΅ΡΠΈΠΎΠ΄Π° Ρ ΠΊΠΎΠΌΠ΅ ΡΡ ΠΎΡΡΠ²Π°ΡΠ΅Π½ΠΈ ΡΡΠ»ΠΎΠ²ΠΈ Π·Π° ΠΏΠΎΡΠ°Π²Ρ
Π±ΠΎΠ»Π΅ΡΡΠΈ ΠΈ Π²ΡΠ΅ΠΌΠ΅Π½ΡΠΊΠΎΠ³ ΡΡΠ΅Π½ΡΡΠΊΠ° Ρ ΠΊΠΎΠΌΠ΅ ΡΠ΅ ΠΏΠΎΡΡΠ΅Π±Π½ΠΎ ΠΎΠ±Π°Π²ΠΈΡΠΈ Ρ
Π΅ΠΌΠΈΡΡΠΊΠ΅ ΡΡΠ΅ΡΠΌΠ°Π½Π΅
ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΡΠ° ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ°Π½ ΠΏΡΠΎΠ±Π»Π΅ΠΌ Π·Π±ΠΎΠ³ ΡΠ»ΠΎΠΆΠ΅Π½ΠΎΡΡΠΈ ΠΊΡΠ΅ΠΈΡΠ°ΡΠ° ΠΏΡΠ΅Π΄ΠΈΠΊΡΠΈΠΎΠ½ΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π° ΡΠΈΡΠΈ ΡΠ΅ Π·Π°Π΄Π°ΡΠ°ΠΊ Π΄Π΅ΡΠΈΠ½ΠΈΡΠ°ΡΠ΅ Π²Π΅Π·Π° ΠΈΠ·ΠΌΠ΅ΡΡ ΠΏΠΎΡΠ°Π²Π΅ Π±ΠΎΠ»Π΅ΡΡΠΈ ΠΈ ΡΡΠ΅Π½ΡΡΠ½ΠΈΡ
ΠΌΠ΅ΡΠ΅ΠΎΡΠΎΠ»ΠΎΡΠΊΠΈΡ
ΡΡΠ»ΠΎΠ²Π°. Π’Π°ΡΠ½ΠΎΡΡ ΠΏΡΠ΅Π΄ΠΈΠΊΡΠΈΡΠ΅ Π²ΡΠ΅ΠΌΠ΅Π½Π° Ρ
Π΅ΠΌΠΈΡΡΠΊΠΈΡ
ΡΡΠ΅ΡΠΌΠ°Π½Π° Π΄ΠΈΡΠ΅ΠΊΡΠ½ΠΎ ΡΡΠΈΡΠ΅ Π½Π° Π΅ΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ½ΠΎΡΡ ΠΏΠΎΡΠΎΠΏΡΠΈΠ²ΡΠ΅Π΄Π½Π΅ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΠ΅ ΠΈ ΠΊΠΎΠ»ΠΈΡΠΈΠ½Π΅ ΠΏΠΎΡΡΠ΅Π±Π½ΠΈΡ
ΠΏΠ΅ΡΡΠΈΡΠΈΠ΄Π°, ΡΠ΅ Π΄ΠΎΠΏΡΠΈΠ½ΠΎΡΠΈ Π·Π°ΡΡΠΈΡΠΈ ΠΆΠΈΠ²ΠΎΡΠ½Π΅ ΡΡΠ΅Π΄ΠΈΠ½Π΅, ΠΊΠ°ΠΎ ΠΈ Π·Π΄ΡΠ°Π²ΠΈΡΠΈΠΌ ΠΏΠΎΡΠΎΠΏΡΠΈΠ²ΡΠ΅Π΄Π½ΠΈΠΌ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠΌΠ°. Π‘ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠΌ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ΅ΠΌ ΡΠ΅ ΠΊΡΠ΅ΠΈΡΠ°Π½ΠΎ ΡΠΎΡΡΠ²Π΅ΡΡΠΊΠΎ ΡΠ΅ΡΠ΅ΡΠ΅, Π±Π°Π·ΠΈΡΠ°Π½ΠΎ Π½Π° ΠΏΡΠΈΠΌΠ΅Π½ΠΈ data mining ΡΠ΅Ρ
Π½ΠΈΠΊΠ° ΠΈ Π±Π΅ΠΆΠΈΡΠ½ΠΈΡ
ΠΊΠΎΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΎΠ½ΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ°, ΡΠΈΡΠΈ ΡΠ΅ Π·Π°Π΄Π°ΡΠ°ΠΊ ΠΏΡΠΈΠΊΡΠΏΡΠ°ΡΠ΅ ΠΌΠ΅ΡΠ΅ΠΎΡΠΎΠ»ΠΎΡΠΊΠΈΡ
ΠΈ ΠΏΡΠΎΡΡΠΎΡΠ½ΠΎ-Π²ΡΠ΅ΠΌΠ΅Π½ΡΠΊΠΈΡ
ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΠ°ΡΠ°, Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΠΊΠΎΡΠΈΡ
ΡΠ΅ Π²ΡΡΠΈ ΠΏΡΠ΅Π΄ΠΈΠΊΡΠΈΡΠ° ΠΎΡΡΠ²Π°ΡΠ΅Π½ΠΎΡΡΠΈ ΡΡΠ»ΠΎΠ²Π° Π·Π° ΠΏΠΎΡΠ°Π²Ρ Π±ΠΈΡΠ½ΠΈΡ
Π±ΠΎΠ»Π΅ΡΡΠΈ, Π° ΡΠ°ΠΌΠΈΠΌ ΡΠΈΠΌ ΠΈ ΠΏΡΠ΅Π΄ΠΈΠΊΡΠΈΡΠ° Π²ΡΠ΅ΠΌΠ΅Π½Π° Ρ
Π΅ΠΌΠΈΡΡΠΊΠΈΡ
ΡΡΠ΅ΡΠΌΠ°Π½Π°. Π Π°Π·Π²ΠΈΡΠ΅Π½ΠΎ ΡΠ΅ΡΠ΅ΡΠ΅ ΡΠ΅ Π·Π°ΡΠ²ΠΎΡΠ΅Π½ΠΎΠ³ ΡΠΈΠΏΠ°, ΠΎΠ΄Π½ΠΎΡΠ½ΠΎ Ρ ΠΎΠΊΠ²ΠΈΡΡ ΠΈΡΡΠΎΠ³ ΡΠ΅ Π²ΡΡΠΈ ΠΏΡΠΈΠΊΡΠΏΡΠ°ΡΠ΅ ΠΏΠΎΡΡΠ΅Π±Π½ΠΈΡ
ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ°, ΠΎΠ±ΡΠ°Π΄Π° ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° Ρ ΡΠΈΡΡ ΠΊΡΠ΅ΠΈΡΠ°ΡΠ° ΠΏΡΠ΅Π΄ΠΈΠΊΡΠΈΠΎΠ½ΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π° ΠΈ ΠΏΡΠΈΠΌΠ΅Π½Π° ΡΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π° Π·Π° ΠΏΡΠ΅Π΄ΠΈΠΊΡΠΈΡΡ Π²ΡΠ΅ΠΌΠ΅Π½Π° Ρ
Π΅ΠΌΠΈΡΡΠΊΠΈΡ
ΡΡΠ΅ΡΠΌΠ°Π½Π°. ΠΠ°ΠΊΠΎ Π±ΠΈ ΡΠ΅ ΡΡΠ²ΡΠ΄ΠΈΠ»Π° ΡΠ°ΡΠ½ΠΎΡΡ ΠΏΡΠ΅Π΄ΠΈΠΊΡΠΈΡΠ΅, ΠΈΠ·Π²ΡΡΠ΅Π½ΠΎ ΡΠ΅ ΡΠ΅ΡΡΠΈΡΠ°ΡΠ΅ Π΄ΠΎΠ±ΠΈΡΠ΅Π½ΠΈΡ
ΠΏΡΠ΅Π΄ΠΈΠΊΡΠΈΠΎΠ½ΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π° Π·Π° ΠΊΡΠ΅ΠΈΡΠ°Π½Π΅ ΡΠΊΡΠΏΠΎΠ²Π΅ ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ°. ΠΠ°ΠΊΡΡΡΠ΅Π½ΠΎ ΡΠ΅ Π΄Π° ΡΠ°ΡΠ½ΠΎΡΡ ΠΏΡΠ΅Π΄ΠΈΠΊΡΠΈΡΠ΅ ΠΈΠ·Π½ΠΎΡΠΈ 93,71%, ΡΡΠΎ ΠΎΠΏΡΠ°Π²Π΄Π°Π²Π° ΠΊΠΎΡΠΈΡΡΠ΅ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΠ° Π·Π°ΡΠ½ΠΎΠ²Π°Π½ΠΎΠ³ Π½Π° data mining ΡΠ΅Ρ
Π½ΠΈΠΊΠ°ΠΌΠ° ΠΈ Π±Π΅ΠΆΠΈΡΠ½ΠΈΠΌ ΠΊΠΎΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΎΠ½ΠΈΠΌ ΡΠΈΡΡΠ΅ΠΌΠΈΠΌΠ° Π·Π° ΠΏΡΠ΅Π΄ΠΈΠΊΡΠΈΡΡ Π²ΡΠ΅ΠΌΠ΅Π½Π° Ρ
Π΅ΠΌΠΈΡΡΠΊΠΈΡ
ΡΡΠ΅ΡΠΌΠ°Π½Π°