Precision Agriculture using Internet of Thing with Artificial Intelligence: A Systematic Literature Review

Zahid Hussain Khand,Sana Gul,Manisha Kumari,Ghulam Mujtaba Sheikh,Noureen Fatima,Kainat Fareed Memon International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.7

Machine learning with its high precision algorithms, Precision agriculture (PA) is a new emerging concept nowadays. Many researchers have worked on the quality and quantity of PA by using sensors, networking, machine learning (ML) techniques, and big data. However, there has been no attempt to work on trends of artificial intelligence (AI) techniques, dataset and crop type on precision agriculture using internet of things (IoT). This research aims to systematically analyze the domains of AI techniques and datasets that have been used in IoT based prediction in the area of PA. A systematic literature review is performed on AI based techniques and datasets for crop management, weather, irrigation, plant, soil and pest prediction. We took the papers on precision agriculture published in the last six years (2013-2019). We considered 42 primary studies related to the research objectives. After critical analysis of the studies, we found that crop management; soil and temperature areas of PA have been commonly used with the help of IoT devices and AI techniques. Moreover, different artificial intelligence techniques like ANN, CNN, SVM, Decision Tree, RF, etc. have been utilized in different fields of Precision agriculture. Image processing with supervised and unsupervised learning practice for prediction and monitoring the PA are also used. In addition, most of the studies are forfaiting sensory dataset to measure different properties of soil, weather, irrigation and crop. To this end, at the end, we provide future directions for researchers and guidelines for practitioners based on the findings of this review.

Effect of light illumination and camera moving speed on soil image quality

정선옥,조기현,정기열 충남대학교 농업과학연구소 2012 농업과학연구 Vol.39 No.3

Soil texture has an important influence on agriculture such as crop selection, movement of nutrient and water,soil electrical conductivity, and crop growth. Conventionally, soil texture has been determined in the laboratory using pipette and hydrometer methods requiring significant amount of time, labor, and cost. Recently, in-situ soil texture classification systems using optical diffuse reflectometry or mechanical resistance have been reported, especially for precision agriculture that needs more data than conventional agriculture. This paper is a part of overall research to develop an in-situ soil texture classification system using image processing. Issues investigated in this study were effects of sensor travel speed and light source and intensity on image quality. When travel speed of image sensor increased from 0 to 10 mm/s, travel distance and number of pixel were increased to 3.30 mm and 9.4, respectively. This travel distances were not negligible even at a speed of 2 mm/s (i.e., 0.66 mm and 1.4), and image degradation was significant. Tests for effects of illumination intensity showed that 7 to 11 Lux seemed a good condition minimizing shade and reflection. When soil water content increased, illumination intensity should be greater to compensate decrease in brightness. Results of the paper would be useful for construction, test, and application of the sensor. Soil texture has an important influence on agriculture such as crop selection, movement of nutrient and water,soil electrical conductivity, and crop growth. Conventionally, soil texture has been determined in the laboratory using pipette and hydrometer methods requiring significant amount of time, labor, and cost. Recently, in-situ soil texture classification systems using optical diffuse reflectometry or mechanical resistance have been reported, especially for precision agriculture that needs more data than conventional agriculture. This paper is a part of overall research to develop an in-situ soil texture classification system using image processing. Issues investigated in this study were effects of sensor travel speed and light source and intensity on image quality. When travel speed of image sensor increased from 0 to 10 mm/s, travel distance and number of pixel were increased to 3.30 mm and 9.4, respectively. This travel distances were not negligible even at a speed of 2 mm/s (i.e., 0.66 mm and 1.4), and image degradation was significant. Tests for effects of illumination intensity showed that 7 to 11 Lux seemed a good condition minimizing shade and reflection. When soil water content increased, illumination intensity should be greater to compensate decrease in brightness. Results of the paper would be useful for construction, test, and application of the sensor.

최신 농업기계 특허 동향 조사

홍순중,김동억,강동현,김진진,강정균,이경환,모창연,류동기,Hong, S.J.,Kim, D.E.,Kang, D.H.,Kim, J.J.,Kang, J.G.,Lee, K.H.,Mo, C.Y.,Ryu, D.K. 국립한국농수산대학교 교육개발센터 2021 현장농업연구지 = Journal of practical agricultural resear Vol.23 No.2

농경지, 농기계, 농작업자 간 IoT 등의 통신 기술을 이용한 유기적인 정보교환을 통해 생산성, 효율성, 수익성을 높이는 지능형 데이터 농업 형태인 커넥티드 팜이 상용화 단계에 있다. 본 연구는 지능형 농업기계의 교육과정과 농업기계 안전교육 성과지표를 개발하고자 ICT, 로봇, 인공지능 등 첨단 기술을 적용한 농업생산의 무인화 및 고효율화 변화에 따른 농업기계의 특허 동향을 조사 분석하였다. 노지용 자동화 기술과 관련해서 미국, 일본, 유럽, 한국의 특허 건수는 각각 541건, 326건, 128건, 85건으로 미국에서의 특허 활동이 가장 활발한 것으로 나타났고, 일본, 유럽, 한국의 순으로 조사되어 한국에서의 농업 자동화 기술이 선진국에 비해 뒤쳐져있는 것으로 조사되었다. 노지 자동화 기술의 세분기술 분야로 보면, 경로 생성 및 추종 기술, 환경 인식을 통한 작업기 제어 기술, 로봇 농작업 시스템 설계 기술, 작물 및 환경 센싱 기술, 수확량 및 품질 모니터링 기술 분야 순으로 출원 점유율이 높은 것으로 나타났다. The connected farm that agricultural land, agricultural machinery and farmer are connected with an IoT gateway is in the commercialization stage. That has increased productivity, efficiency and profitability by intimate information exchange among those. In order to develop the educational program of intelligent agricultural machinery and the agricultural machinery safety education performance indicator, this study analyzed patent trends of agricultural machine with unmanned technology used in agriculture and efficiency technology applied advanced technologies such as ICT, robots and artificial intelligence. We investigated and analyzed patent trends in agricultural machinery of Korea, the USA and Japan as well as the countries in Europe. The United States is an advanced country in the field of unmanned technology and efficiency technology used in agriculture. Agricultural automation technology in Korea is insufficient compared to developed countries, which means rapid technological development is needed. In the sub-fields of field automation technology, path generation and following technology and working machine control technology through environmental awareness have activated.

A Comprehensive Literature Study on Precision Agriculture: Tools and Techniques

Bh., Prashanthi,A.V. Praveen, Krishna,Ch. Mallikarjuna, Rao International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.12

Due to digitization, data has become a tsunami in almost every data-driven business sector. The information wave has been greatly boosted by man-to-machine (M2M) digital data management. An explosion in the use of ICT for farm management has pushed technical solutions into rural areas and benefited farmers and customers alike. This study discusses the benefits and possible pitfalls of using information and communication technology (ICT) in conventional farming. Information technology (IT), the Internet of Things (IoT), and robotics are discussed, along with the roles of Machine learning (ML), Artificial intelligence (AI), and sensors in farming. Drones are also being studied for crop surveillance and yield optimization management. Global and state-of-the-art Internet of Things (IoT) agricultural platforms are emphasized when relevant. This article analyse the most current publications pertaining to precision agriculture using ML and AI techniques. This study further details about current and future developments in AI and identify existing and prospective research concerns in AI for agriculture based on this thorough extensive literature evaluation.

무인항공기를 이용한 농경지 모니터링 시스템

강병준(Byung-Jun Kang),조현찬(Hyun-Chan Cho) 한국산학기술학회 2016 한국산학기술학회논문지 Vol.17 No.6

본 연구의 목적은 농경지 상태 이미지 취득 장치와 농작업 데이터, 날씨 데이터를 데이터베이스화하여 관리할 수 있는 시스템을 구성하는 것이다. 농업관련 외국 회사들은 이미 다양한 방법으로 농업에 관한 데이터베이스를 구축하고 농업 의 과학화를 이루어내고 있다. 본 연구의 시스템의 구성은 무인항공기에 탑재되는 GPS와 디지털카메라, PC를 이용한 영상 취득 장치, 취득한 여러 영상을 하나의 이미지로 정합하는 부분, GPS와 정합된 영상 간 매칭, 최종적으로 일자별 기상청 날씨정보와 농작업 데이터, 이미지를 데이터베이스화 하는 부분으로 구성된다. 본 연구의 결과로 우리나라 농업의 총 생산량 만의 데이터가 아닌 기후와 농작업 데이터 등의 요인과 함께 농경지 이미지로써 결과 확인 및 데이터베이스화 할 수 있는 시스템을 제안하였다. 제안한 시스템을 통해 인공위성 사진에 비하여 최대 약 5배 좋은 화질의 이미지를 얻을 수 있었으며, 농작업과 환경요인 등이 농경지 전체에 미치는 영향 분석 사용 될 기초 데이터를 얻을 수 있었다. 무인항공기를 이용한 농경 지 모니터링 시스템을 통하여 우리나라 농업의 과학적 분석에 기여할 것으로 기대된다. The purpose of this study is to develop a system configuration for gathering data and building a database for agriculture. Some foreign agriculture-related companies have already constructed such a database for scientific agriculture. The hardware of this system is composed of automatic capturing equipment based on aerial photography using a UAV. The software is composed of parts for stitching images, matching GPS data with captured images, and building a database of collected weather information, farm operation data, and aerial images. We suggest a method for building the database, which can include information about the amount of agricultural products, weather, farm operation, and agricultural land images. The images of this system are about 5 times better than satellite images. Factors such as farm working and environmental factors can be basic data for analyzing the full impact of agriculture land. This system is expected to contribute to the scientific analysis of Korea's agriculture.

Evaluating the Accuracy of FOV Alignment for Micasense Multispectral Imagery in VI Calculation

( Md Asrakul Haque ),( Md Rejaul Karim ),( Md Razob Ali ),( Shaha Nur Kabir ),( Keong Do Lee ),( Yeong Ho Kang ),( Sun-ok Chung ) 한국농업기계학회 2023 한국농업기계학회 학술발표논문집 Vol.28 No.2

Multispectral imagery is pivotal for vegetation index (VI) analysis, shaping crop nutritional management strategies and advancing precision agriculture. Yet, the efficacy of image enhancement techniques in VI calculation remains a critical inquiry. This study addresses this gap by evaluating various image enhancement methods for multispectral imagery, focusing on the widely accepted Normalized Differential Vegetation Index (NDVI). We employed a multispectral sensor, the MicaSense RedEdge MX, alongside an active sensor, the Crop-circle ACS-435, to assess NDVI calculation performance. Our objective was to assess the accuracy of the Field of View (FOV) alignment of MicaSense with the active sensor. Data collection occurred across four distinct wheat growth stages (GS1, GS2, GS3, and GS4) utilizing a handheld structure equipped with Crop Circle ACS 435, MicaSense RedEdge MX, and a Topcon Hiper VR GNSS rover. This setup maintained a consistent 90cm canopy height based on the plot width. Python programming facilitated GPS location processing and image segmentation based on pixel coordinates, mirroring the Crop-circle FOV. We extracted reflectance data from the segmented portion of each band and calculated NDVI using Red and NIR reflectance data. Data enhancement techniques were assessed by comparing enhanced and raw image data against standardized data from the Crop-circle sensor. Regression analysis, including the coefficient of determination (R2) and root mean square error (RMSE), was utilized for evaluation. The application of the FOV enhancement technique to MicaSense images yielded significant improvements in regression metrics (R2 and RMSE) across GS1, GS2, GS3, and GS4. Notably, FOV enhancement resulted in R2 increases of 50%, 18%, 16%, and 4% and RMSE values of0.06, 0.05, 0.06, and 0.03, respectively. The most substantial accuracy enhancements were observed in GS1 (50%), indicating varying effectiveness based on vegetation growth stage and density. This study underscores the critical role of multispectral imagery and the efficacy of FOV alignment in improving NDVI calculation accuracy. These findings hold valuable implications for future research and precision agriculture practices.

Development of Nondestructive Grouping System for Soil Organic Matter Using VIS and NIR Spectral Reflectance

Sung J.H. Korean Society for Agricultural Machinery 2005 Agricultural and Biosystems Engineering Vol.6 No.1

This study was conducted to develop a nondestructive grouping system for soil organic matter using visible (VIS) and near infrared (NIR) spectroscopic method. The artificial light was irradiated on the cut soil surface at 15 to 20 cm depths to reduce the errors of light at open field. The reflectance energy from the cut soil surface was measured to group the soil organic matter using VIS/NIR light sensor with narrow band pass filter. From reflectance spectra of soil samples, the sensitive wavelengths for measuring the soil organic matter were selected and compared to previous research results. The grouping system for soil organic matter consisted of light sensor with band pass filter measuring the reflectance energy of the cut soil surface, global positing system (GPS), analog-to-digital (AD) converter, computer and operating software. The regression models to predict the soil organic matter were developed and evaluated. From field test, the accuracies of the developed light sensor system were 81.3% for five-stage grouping of the soil organic matters and 91.0% for three-stages grouping of the soil organic matters, respectively. It could be possible to support the decision making for variable rate applications with the developed grouping system for soil organic matter in precision agriculture.

Potential of impact-based mass estimation of individual radish tubers for real-time yield monitoring

키라가샤피크 ( Shafik Kiraga ),구란다즈아스라푸자만 ( Ashraffuzaman Gulandaz ),카비르사자둘 ( Kabir Sazzadul ),레자나심 ( Nasim Reza ),정선옥 ( Sun-ok Chung ) 한국농업기계학회 2022 한국농업기계학회 학술발표논문집 Vol.27 No.1

Yield monitoring provides information on the spatial variability of yield in the field and it is one of the basic components of precision agriculture. The objective of this study was to investigate the effects of different harvesting conditions on radish mass measurements using a double load cell impact plate. The harvesting conditions included the falling height, conveyor speed, and impact plate angle, which were simulated using an impact plate attached to a laboratory test bench. The relative error (RE), standard error (SE), and the coefficient of determination (R2) were the statistical indicators used to describe the accuracy of the estimates. Analysis of variance (ANOVA) without interaction of factors and the Duncan multiple range tests were performed using the above indicators except R2. The falling height and conveyor speed had no significant effect on radish mass measurement. In contrast, the impact plate angle significantly affected the impact plate precision. Minimum and maximum standard error of 1.68 and 4.39 were obtained at -100, 40 cm, 0.05 m/s and -500, 30 cm, 0.25 m/s, respectively. The results showed the possibility of using impact-based sensors for individual measurement of radish for real-time yield monitoring.

Development of a Real-time Grouping System of Rice Crop Canopy Chlorophyll Contents

Sung J.H.,Jung I.G.,Lee C.K. Korean Society for Agricultural Machinery 2005 Agricultural and Biosystems Engineering Vol.6 No.1

This study was carried out to develop a real-time grouping system of chlorophyll contents of rice crop canopy for precision agriculture. The system measured reflected light energy of a rice canopy on a paddy field from visual to near-infrared range and analyzed the collected information of chlorophyll contents of rice crop canopy with given position data. The four filters, 560 nm $({\pm}10nm)$, 650 nm $({\pm}25nm)$, 700 nm $({\pm}12nm)$, and 850 nm $({\pm}40nm)$, were used for a multiple regression to estimate the chlorophyll contents of rice crop canopy. Every $0.2m^2$ area of the open field was inspected at a distance of 1 m above the rice canopy. According to the results of verification test, the chlorophyll content grouping by a commerical chlorophyll meter (SPAD) and by the developed system showed 58.7% match for five-stage chlorophyll contents of rice crop canopy grouping and 93.5% for the $five{\pm}1-stage$ grouping. In addition, the results showed 63.0% match for three-stage grouping and 100.0% for the $three{\pm}1-stage$ grouping.

노지 농업의 환경 요소 분석을 위한 스마트 IoT 시스템 구현에 관한 연구

이병주(Byungju Lee),곽윤식(Yoonsik Kwak) 한국정보기술학회 2022 한국정보기술학회논문지 Vol.20 No.2

This paper is about for the precision agriculture according to the development trends of agriculture. In case of the precision agriculture, it is the goal to collect the information about growth environment factors for crops and get the productivity, economics ans merchandising based on optimized growth environment. In order to these goal we design and implement the smart IoT analysis system that consist of various sensors related with the information about environmental factors(temperture, humidity, weather condition etc-atmosphere and soil) and then can acquize the environmental factors(atmosphere and soil) and useful information(factors for optimizing control). Also the quality of agricultural industry technology could be expected to improve through additional research.