Using Machine Learning to Integrate On-Farm Sensors and Agro-Meteorology Networks into Site-Specific Decision Support

HighlightsMachine learning can incorporate a variety of data from low-cost sensors and estimate actual ET by comparison with short-term, higher-cost measurements.On-farm weather monitoring can be leveraged to estimate site-specific crop-water requirements.Expanding spatial coverage of weather and actual ET through on-farm monitoring will facilitate localization and leverage publicly available weather data to guide irrigation decisions and improve irrigation water management.Abstract. One of the basic challenges to adopting science-based irrigation scheduling is providing reliable, site-specific estimates of actual crop water demand. While agro-meteorology networks cover most agricultural production areas in the U.S., widely spaced stations represent regionally specific, rather than site-specific, conditions. A variety of low to moderate cost commercial weather stations are available but do not provide directly useful information, such as actual evapotranspiration (ETa), or the ability to incorporate additional sensors. We demonstrate that machine learning methods can provide real-time, site-specific information about ETa and crop water demand using on-farm sensors and public weather information. Two years of field experiments were conducted at four irrigated field sites with crops including snap beans, alfalfa, and pasture. On-farm data were compared to publicly available data originating at nearby agro-meteorology network stations. The machine learning procedure can robustly estimate ETa using data from a few basic sensors, but the resulting estimate is sensitive to the range of conditions that are used as training data. The results demonstrate that machine learning can be used with affordable sensors and publicly available data to improve local estimates of crop water demand when high-quality measurements can be co-located for short periods of time. Supplementary sensors can also be integrated into a tailored monitoring plan to estimate crop stress and other operational considerations. Keywords: Agro-meteorology, Irrigation requirement, Machine learning, Site-specific Irrigation.

Download Full-text

Machine Learning Method for Forecasting Weather Needed For Crop Water Demand Estimations in Low-Resource Settings Using A Case Study in Morocco

10.1115/detc2021-70571 ◽

2021 ◽

Author(s):

Carolyn Sheline ◽

Amos Winter

Keyword(s):

Machine Learning ◽

Water Demand ◽

Weather Forecasting ◽

Weather Prediction ◽

Crop Productivity ◽

Irrigation Scheduling ◽

Crop Water ◽

Low Resource Settings ◽

Low Resource ◽

Crop Water Demand

Abstract Low and middle income countries often do not have the infrastructure needed to support weather forecasting models, which are computationally expensive and often require detailed inputs from local weather stations. Local, low-cost weather prediction services are needed to enable optimal irrigation scheduling and increase crop productivity for rural farmers in low-resource settings. This work proposes a machine learning approach to predict the weather inputs needed to calculate crop water demand, namely evapotranspiration and precipitation. The focus of this work is on the accuracy with which Moroccan weather can be predicted with a vector autoregression (VAR) model compared to using typical meteorological year (TMY) weather, and how this accuracy changes as the number of weather parameters is reduced.

Download Full-text

Using Neural Networks to Estimate Site-Specific Crop Evapotranspiration with Low-Cost Sensors

Agronomy ◽

10.3390/agronomy9020108 ◽

2019 ◽

Vol 9 (2) ◽

pp. 108 ◽

Cited By ~ 6

Author(s):

Jason Kelley ◽

Eric Pardyjak

Keyword(s):

Neural Networks ◽

Eddy Covariance ◽

Water Demand ◽

Irrigation Management ◽

Crop Water ◽

Eddy Covariance Method ◽

Site Specific ◽

Training Time ◽

Crop Water Demand ◽

On Farm

Irrigation efficiency is facilitated by matching irrigation rates to crop water demand based on estimates of actual evapotranspiration (ET). In production settings, monitoring of water demand is typically accomplished by measuring reference ET rather than actual ET, which is then adjusted approximately using simplified crop coefficients based on calendars of crop maturation. Methods to determine actual ET are usually limited to use in research experiments for reasons of cost, labor and requisite user skill. To pair monitoring and research methods, we co-located eddy covariance sensors with on-farm weather stations over two different irrigated crops (vegetable beans and hazelnuts). Neural networks were used to train a neural network and utilize on-farm weather sensors to report actual ET as measured by the eddy covariance method. This approach was able to robustly estimate ET from as few as four sensor parameters (temperature, solar radiation, humidity and wind speed) with training time as brief as one week. An important limitation found with this machine learning method is that the trained network is only valid under environmental and crop conditions similar to the training period. The small number of required sensors and short training times demonstrate that this approach can estimate site-specific and crop specific ET. With additional field validation, this approach may offer a new method to monitor actual crop water demand for irrigation management.

Download Full-text

A Practical Guide for Peach Irrigation Scheduling in Florida

EDIS ◽

10.32473/edis-hs1413-2021 ◽

2021 ◽

Vol 2021 (3) ◽

Author(s):

Lincoln Zotarelli ◽

Carlos Zambrano-Vaca ◽

Charles E. Barrett ◽

Vivek Sharma ◽

Juanita Popenoe ◽

...

Keyword(s):

Water Demand ◽

Field Research ◽

Irrigation Scheduling ◽

Growth Stages ◽

Peach Tree ◽

Crop Water ◽

Central Florida ◽

Crop Water Demand ◽

Practical Information ◽

Peach Trees

The goal of this publication is to provide a practical guideline for irrigation of young (1–3 years old) and adult (>3 years old) peach trees cultivated in Florida. This document is based on field research of peach water uptake conducted by UF/IFAS. The first section describes peach tree growth stages and their respective crop water demand in central Florida. The second and third sections present practical information on preparing year-round irrigation scheduling for young and adult peach trees, respectively. More information about irrigation practices for peaches is provided in EDIS publication HS1316 (https://edis.ifas.ufl.edu/hs1316).

Download Full-text

Canal delivery and irrigation scheduling optimization based on crop water demand

Agricultural Water Management ◽

10.1016/j.agwat.2021.107245 ◽

2022 ◽

Vol 260 ◽

pp. 107245

Author(s):

Fuqiang Zhang ◽

Chao He ◽

Fan Yaqiong ◽

Xinmei Hao ◽

Shaozhong Kang

Keyword(s):

Water Demand ◽

Irrigation Scheduling ◽

Crop Water ◽

Scheduling Optimization ◽

Crop Water Demand

Download Full-text

Machine learning based crop water demand forecasting using minimum climatological data

Multimedia Tools and Applications ◽

10.1007/s11042-019-08533-w ◽

2020 ◽

Vol 79 (19-20) ◽

pp. 13109-13124

Author(s):

Ravneet Kaur Sidhu ◽

Ravinder Kumar ◽

Prashant Singh Rana

Keyword(s):

Machine Learning ◽

Water Demand ◽

Demand Forecasting ◽

Crop Water ◽

Climatological Data ◽

Water Demand Forecasting ◽

Crop Water Demand

Download Full-text

Future Climatic Patterns and Sustainability of Current Cropping Patterns in a Water-Scarce River Basin of Eastern India

10.5194/egusphere-egu21-10487 ◽

2021 ◽

Author(s):

Smaranika Mahapatra ◽

Madan Kumar Jha

Keyword(s):

Water Demand ◽

Agricultural Sector ◽

Monsoon Rainfall ◽

Baseline Period ◽

Eastern India ◽

Crop Water ◽

Cropping Patterns ◽

Water Demands ◽

Crop Water Demand ◽

Water Scarce

Agricultural sector, being the largest consumer of water is greatly affected by climatic variability and disasters. Most parts of the world already face an enormous challenge in meeting competitive and conflicting multi-sector water demands. Climate change has further exacerbated this challenge by putting the sustainability of current cropping patterns and irrigation practices in question. For ensuring climate-resilient food production, it is crucial to examine the patterns of the projected climate and potential impacts on the agricultural sector at a basin scale. Hence, this study was carried out for an already water-scarce basin, Rushikulya River basin (RRB), located in the coastal region of eastern India. The bias-corrected NorESM2-MM general circulation model of Coupled Model Intercomparison Project-6 (CMIP6) was used in this study under four shared socioeconomic pathway (SSPs) scenarios, namely SSP126, SSP245, SSP370 and SSP585. The projected climatic parameters and crop water demands of the basin were analyzed assuming existing cropping pattern in the future. Analysis of the results reveals a significant and rapid increase in the temperature at a rate of 0.02-0.5&#186;C/year during 2026-2100 under all SSPs except SSP126, whereas the rainfall is expected to increase slightly during 2026-2100 as compared to the baseline period (1990-2016), especially in the far future (2076-2100) under all the SSPs. In contrast, monsoon rainfall is predicted to decrease under SSP245 and SSP370, while a slight increase in the monsoon rainfall is evident under SSP126 and SSP585. Although the rainy days will decrease slightly in the future 25-year time window, the number of heavy rainfall events is predicted to increase by two to three times. Also, retrospective analysis of rainfall and evapotranspiration suggested an existence of rainfall deficit (rainfall-evapotranspiration) in the basin throughout the year, except during July to September. The rainfall deficit in the basin during 2026-2100 is found to remain more or less same in the non-monsoon season, except for the month of October under SSP245, SSP370 and SSP585 scenarios where deficit increases by two folds. Rainfall is expected to be in surplus by 4 to 5 times higher under all SSPs except for SSP245. As to the evapotranspiration, an insignificant increasing trend is observed under future climatic condition with only 2 to 4% rise in the crop water demand compared to the baseline period. As the basin is already water stressed during most months in a year under baseline and future climatic conditions, continuing the current practice of monsoon paddy dominant cultivation in the basin will further aggravate this situation. The results of this study will be helpful in formulating sustainable irrigation plans and adaptation measures to address climate-induced water stress in the basin.Keywords: Climate change; CMIP6; SSP; Monsoon rainfall; Temperature; Crop water demand.

Download Full-text

DEMANDA HÍDRICA DO MARACUJAZEIRO AMARELO (Passiflora edulis Sims f. flavicarpa Deg.)

Irriga ◽

10.15809/irriga.2002v7n3p185-190 ◽

2002 ◽

Vol 7 (3) ◽

pp. 185-190

Author(s):

Ana Alexandrina Gama da Silva ◽

Antonio Evaldo Klar

Keyword(s):

Water Demand ◽

Crop Coefficient ◽

Passion Fruit ◽

Crop Water ◽

Passiflora Edulis ◽

Crop Water Demand ◽

Passiflora Edulis Sims ◽

E Mail ◽

Fruit Formation ◽

Yellow Passion Fruit

DEMANDA HÍDRICA DO MARACUJAZEIRO AMARELO (Passiflora edulis Sims f. flavicarpa Deg.) Ana Alexandrina Gama da SilvaEmbrapa Tabuleiros Costeiros, CP 44, CEP 49025-040, Aracaju, SE. E-mail: [email protected] Evaldo KlarDepartamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista, CP 237, CEP 18603-970, Botucatu, SP. E-mail: [email protected] Científico do CNPq 1 RESUMO Determinou-se à demanda hídrica e o coeficiente de cultivo (Kc) do maracujá amarelo (Passiflora edulis Sims f. flavicarpa Deg.), seleção Sul-Brasil, cultivado sob irrigação localizada, no município de Botucatu-SP (22o 51’ S, 48o 26’ W). A evapotranspiração máxima da cultura (ETc) e a evapotranspiração de referência (ETo) foram medidas em lisímetros de nível de lençol freático constante, durante o período de 29 de setembro de 2000 a 20 de julho de 2001. Os valores da ETc e ETo foram de 954,98 mm e 1.069,21 mm, respectivamente, durante todo o período medido. Os valores de Kc variaram de 0,42 a 1,12, com os valores máximos registrados entre 150 e 210 dias após o transplantio das mudas no campo (DAT), período correspondente aos estádios fenológicos de florescimento e formação dos frutos. UNITERMOS: Passiflora edulis, evapotranspiração, coeficiente de cultivo (Kc). SILVA, A.A.G. da, KLAR, A.E. YELLOW PASSION FRUIT (Passiflora edulis Sims f. flavicarpa Deg.) CROP: WATER DEMAND. 2 ABSTRACT Crop water demand and crop water coefficient (Kc = ETc/ETo) of yellow passion fruit were evaluated in constant level lysimeters under drip irrigation from September 21, 2000 to July 31, 2001 in Botucatu-SP. The maximum crop water demand (ETc) and the Reference Evapotranspiration (ETo), measured by lysimeters, were 954.98 mm and 1,069.21 mm, respectively, during all period measured. The values of Kc varied from 0.42 to 1.12 with maximum values registered from 150 to 210 days following transplanting during flowering and fruit formation phases. KEYWORDS: Passiflora edulis, evapotranspiration, crop coefficient (Kc).

Download Full-text