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Evapotranspiration, one of the major elements of the water cycle, is sensitive to climate change. The main objective of this study was to examine the response of reference evapotranspiration (ET0) under various climate change scenarios using artificial neural networks and a general circulation model (GCM) - the Canadian Earth System Model Second Generation (CanESM2). The Hargreaves method was used to calculate ET0 for western, central, and eastern parts of Prince Edward Island. The two input parameters of the Hargreaves method; daily maximum temperature (Tmax), and daily minimum temperature (Tmin) were projected using CanESM2. The Tmax and Tmin were downscaled with the help of statistical downscaling and simulation model (SDSM) for three future periods 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2100) under three representative concentration pathways (RCP’s) including RCP 2.6, RCP P4.5, and RCP 8.5, and the. Temporally, there were major changes in Tmax, Tmin, and ET0 for the 2080s under RCP8.5. The temporal variations in ET0 for all RCPs matched the reports in the literature for other similar locations and for RCP8.5 it ranged from 1.63 (2020s) to 2.29 mm/day (2080s). As a next step, a one-dimensional convolutional neural network (1D-CNN), long-short term memory (LSTM), and multilayer perceptron (MLP) were used for estimating ET0 due to the non-linear behavior of ET0 and the limited meteorological input data. High coefficient of correlation (r > 0.95) values for both calibration and validation periods showed the potential of the artificial neural networks in ET0 estimation. The results of this study will help decision makers and water resource managers to quantify the availability of water in future for the island and to optimize the use of island water resources on a sustainable basis.
Junaid Maqsood; Aitazaz A. Farooque; Farhat Abbas; Travis Esau; Xander Wang; Bishnu Acharya; Hassan Afzaal. Application of Artificial Neural Networks to Project Reference Evapotranspiration under Climate Change Scenarios. 2021, 1 .
AMA StyleJunaid Maqsood, Aitazaz A. Farooque, Farhat Abbas, Travis Esau, Xander Wang, Bishnu Acharya, Hassan Afzaal. Application of Artificial Neural Networks to Project Reference Evapotranspiration under Climate Change Scenarios. . 2021; ():1.
Chicago/Turabian StyleJunaid Maqsood; Aitazaz A. Farooque; Farhat Abbas; Travis Esau; Xander Wang; Bishnu Acharya; Hassan Afzaal. 2021. "Application of Artificial Neural Networks to Project Reference Evapotranspiration under Climate Change Scenarios." , no. : 1.
Soil roughness is one of the most challenging issues in the agricultural domain and plays a crucial role in soil quality. The objective of this research was to develop a computerized method based on stereo vision technique to estimate the roughness formed on the agricultural soils. Additionally, soil till quality was investigated by analyzing the height of plow layers. An image dataset was provided in the real conditions of the field. For determining the soil surface roughness, the elevation of clods obtained from tillage operations was computed using a depth map. This map was obtained by extracting and matching corresponding keypoints as super pixels of images. Regression equations and coefficients of determination between the measured and estimated values indicate that the proposed method has a strong potential for the estimation of soil shallow roughness as an important physical parameter in tillage operations. In addition, peak fitting of tilled layers was applied to the height profile to evaluate the till quality. The results of this suggest that the peak fitting is an effective method of judging tillage quality in the fields.
Afshin Azizi; Yousef Abbaspour-Gilandeh; Tarahom Mesri-Gundoshmian; Aitazaz Farooque; Hassan Afzaal. Estimation of Soil Surface Roughness Using Stereo Vision Approach. Sensors 2021, 21, 4386 .
AMA StyleAfshin Azizi, Yousef Abbaspour-Gilandeh, Tarahom Mesri-Gundoshmian, Aitazaz Farooque, Hassan Afzaal. Estimation of Soil Surface Roughness Using Stereo Vision Approach. Sensors. 2021; 21 (13):4386.
Chicago/Turabian StyleAfshin Azizi; Yousef Abbaspour-Gilandeh; Tarahom Mesri-Gundoshmian; Aitazaz Farooque; Hassan Afzaal. 2021. "Estimation of Soil Surface Roughness Using Stereo Vision Approach." Sensors 21, no. 13: 4386.
Phytoremediation is a cost-effective and environmentally friendly approach that can be used for the remediation of metals in polluted soil. This study used a hedge plant–calico (Alternanthera bettzickiana (Regel) G. Nicholson) to determine the role of citric acid in lead (Pb) phytoremediation by exposing it to different concentrations of Pb (0, 200, 500, and 1000 mg kg−1) as well as in a combination with citric acid concentration (0, 250, 500 µM). The analysis of variance was applied on results for significant effects of the independent variables on the dependent variables using SPSS (ver10). According to the results, maximum Pb concentration was measured in the upper parts of the plant. An increase in dry weight biomass, plant growth parameters, and photosynthetic contents was observed with the increase of Pb application (200 mg kg−1) in soil while a reduced growth was experienced at higher Pb concentration (1000 mg kg−1). The antioxidant enzymatic activities like superoxide dismutase (SOD) and peroxidase (POD) were enhanced under lower Pb concentration (200, 500 mg kg−1), whereas the reduction occurred at greater metal concentration Pb (1000 mg kg−1). There was a usual reduction in electrolyte leakage (EL) at lower Pb concentration (200, 500 mg kg−1), whereas EL increased at maximum Pb concentration (1000 mg kg−1). We concluded that this hedge plant, A. Bettzickiana, has the greater ability to remediate polluted soils aided with citric acid application.
Urooj Kanwal; Muhammad Ibrahim; Farhat Abbas; Muhammad Yamin; Fariha Jabeen; Anam Shahzadi; Aitazaz Farooque; Muhammad Imtiaz; Allah Ditta; Shafaqat Ali. Phytoextraction of Lead Using a Hedge Plant [Alternanthera bettzickiana (Regel) G. Nicholson]: Physiological and Biochemical Alterations through Bioresource Management. Sustainability 2021, 13, 5074 .
AMA StyleUrooj Kanwal, Muhammad Ibrahim, Farhat Abbas, Muhammad Yamin, Fariha Jabeen, Anam Shahzadi, Aitazaz Farooque, Muhammad Imtiaz, Allah Ditta, Shafaqat Ali. Phytoextraction of Lead Using a Hedge Plant [Alternanthera bettzickiana (Regel) G. Nicholson]: Physiological and Biochemical Alterations through Bioresource Management. Sustainability. 2021; 13 (9):5074.
Chicago/Turabian StyleUrooj Kanwal; Muhammad Ibrahim; Farhat Abbas; Muhammad Yamin; Fariha Jabeen; Anam Shahzadi; Aitazaz Farooque; Muhammad Imtiaz; Allah Ditta; Shafaqat Ali. 2021. "Phytoextraction of Lead Using a Hedge Plant [Alternanthera bettzickiana (Regel) G. Nicholson]: Physiological and Biochemical Alterations through Bioresource Management." Sustainability 13, no. 9: 5074.
Climate change is impacting different parts of Canada in a diverse manner. Impacts on temperature, precipitation, and stream flows have been reviewed and discussed region and province-wise. The average warming in Canada was 1.6 °C during the 20th century, which is 0.6 °C above the global average. Spatially, southern and western parts got warmer than others, and temporally winters got warmer than summers. Explicit implications include loss of Arctic ice @ 12.8% per decade, retreat of British Columbian glaciers @ 40–70 giga-tons/year, and sea level rise of 32 cm/20th century on the east coast, etc. The average precipitation increased since 1950s from under 500 to around 600 mm/year, with up to a 10% reduction in Prairies and up to a 35% increase in northern and southern parts. Precipitation patterns exhibited short-intense trends, due to which urban drainage and other hydraulic structures may require re-designing. Streamflow patterns exhibited stability overall with a temporal re-distribution and intense peaks. However, surface water withdrawals were well under sustainable limits. For agriculture, the rainfed and semi-arid regions may require supplemental irrigation during summers. Availability of water is mostly not a limitation, but the raised energy demands thereof are. Supplemental irrigation by water and energy-efficient systems, adaptation, and regulation can ensure sustainability under the changing climate.
Ahmad Bhatti; Aitazaz Farooque; Nicholas Krouglicof; Qing Li; Wayne Peters; Farhat Abbas; Bishnu Acharya. An Overview of Climate Change Induced Hydrological Variations in Canada for Irrigation Strategies. Sustainability 2021, 13, 4833 .
AMA StyleAhmad Bhatti, Aitazaz Farooque, Nicholas Krouglicof, Qing Li, Wayne Peters, Farhat Abbas, Bishnu Acharya. An Overview of Climate Change Induced Hydrological Variations in Canada for Irrigation Strategies. Sustainability. 2021; 13 (9):4833.
Chicago/Turabian StyleAhmad Bhatti; Aitazaz Farooque; Nicholas Krouglicof; Qing Li; Wayne Peters; Farhat Abbas; Bishnu Acharya. 2021. "An Overview of Climate Change Induced Hydrological Variations in Canada for Irrigation Strategies." Sustainability 13, no. 9: 4833.
To understand climate change impacts on Prince Edward Island (PEI), Canada, historical daily precipitation and temperature of the island was investigated between the periods: 1931–60 (1940s), 1961–90 (1970s), and 1991–2020 (2000s) in its eastern, central, and western parts. Observed climatic data were utilized, augmented by some validated modeled data of Pacific Climate Impact Consortium (PCIC) for missing years. Statistically significant warming of the island was found ranging from 1.14°C in the east to 0.75°C in the west from the 1970s to 2000s. The warming trend during the period was distributed throughout the year including winters. In the east, mean monthly temperature significantly increased in all the months except for January, March, and June. Significant increase in temperature was found solely during August (+0.80°C) in central, and for August (+0.64°C), September (+0.99°C), and October (+0.73°C) in western parts. Proportionate increase in annual minimum temperature was greater than the maximum, particularly in eastern (+1.57°C) and central (+0.75°C) parts and thus indicated moderated cold there. Over the same 30‐year period, annual precipitation increased 6 percent in the east but decreased 5 and 8 percent in the central and the western PEI, respectively. The changes in precipitation were not statistically significant, except snowfall reduction (−20%) in the west, which was a statistically significant change. Interannual precipitation variations during wet and dry years having 20 and 80 percent probabilities of exceedance, respectively, ranged 350–470 mm/year during 1991–2020. Rainfall intensities, measured by hourly data, increased from 1.15 to 2.24 mm/hr, on average in central and western parts, respectively, in 2004–17 compared to 1970s. Impacts of the rising temperatures, decreasing precipitation, and uneven and intense rainfalls patterns on water resources and rainfed agriculture need further investigations. Climate change adaptations be included in existing water policies to mitigate the impacts.
Ahmad Zeeshan Bhatti; Aitazaz Ahsan Farooque; Nicholas Krouglicof; Wayne Peters; Bishnu Acharya; Qing Li; M. Sheraz Ahsan. Climate change impacts on precipitation and temperature in Prince Edward Island, Canada. World Water Policy 2021, 7, 9 -29.
AMA StyleAhmad Zeeshan Bhatti, Aitazaz Ahsan Farooque, Nicholas Krouglicof, Wayne Peters, Bishnu Acharya, Qing Li, M. Sheraz Ahsan. Climate change impacts on precipitation and temperature in Prince Edward Island, Canada. World Water Policy. 2021; 7 (1):9-29.
Chicago/Turabian StyleAhmad Zeeshan Bhatti; Aitazaz Ahsan Farooque; Nicholas Krouglicof; Wayne Peters; Bishnu Acharya; Qing Li; M. Sheraz Ahsan. 2021. "Climate change impacts on precipitation and temperature in Prince Edward Island, Canada." World Water Policy 7, no. 1: 9-29.
Heat stress provokes thermal discomfort to people living in semiarid and arid climates. This study evaluates thermal discomfort levels, building design concepts, and some heat mitigation strategies in low-income neighborhoods of Faisalabad, Pakistan. The outdoor and indoor weather data are collected from April to August 2016 using a weather station installed ad hoc in urban settings, and the 52 houses of the five low-income participating communities living in congested and less environment-friendly areas of Faisalabad. The discomfort index values, related to the building design concepts, including (i) house orientation to sunlight and (ii) house ventilation, are calculated from outdoor and indoor dry-bulb and wet-bulb temperatures. Our results show that although June was the hottest month of summer 2016, based on the monthly mean temperature of the Faisalabad region, the month of May produced the highest discomfort levels, which were higher in houses exposed to sunlight and without ventilation. The study also identifies some popular heat mitigation strategies adopted by the five participating low-income communities during various heat-related health complaints. The strategies are gender-biased and have medical, cultural/customary backgrounds. For example, about 52% of the males and 28% of the females drank more water during dehydration, diarrhea, and eye infection. Over 11% and 19% of the males and females, respectively, moved to cooler places during fever. About 43% of the males and 51% of the females took water showers and rested to combat flu (runny nose), headache, and nosebleed. The people did not know how to cure muscular fatigue, skin allergy (from a type of Milia), and mild temperature. Planting trees in an area and developing open parks with greenery and thick canopy trees can be beneficial for neighborhoods resembling those evaluated in this study.
Sana Ehsan; Farhat Abbas; Muhammad Ibrahim; Bashir Ahmad; Aitazaz Farooque. Thermal Discomfort Levels, Building Design Concepts, and Some Heat Mitigation Strategies in Low-Income Communities of a South Asian City. International Journal of Environmental Research and Public Health 2021, 18, 2535 .
AMA StyleSana Ehsan, Farhat Abbas, Muhammad Ibrahim, Bashir Ahmad, Aitazaz Farooque. Thermal Discomfort Levels, Building Design Concepts, and Some Heat Mitigation Strategies in Low-Income Communities of a South Asian City. International Journal of Environmental Research and Public Health. 2021; 18 (5):2535.
Chicago/Turabian StyleSana Ehsan; Farhat Abbas; Muhammad Ibrahim; Bashir Ahmad; Aitazaz Farooque. 2021. "Thermal Discomfort Levels, Building Design Concepts, and Some Heat Mitigation Strategies in Low-Income Communities of a South Asian City." International Journal of Environmental Research and Public Health 18, no. 5: 2535.
Deep learning convolutional neural networks (CNNs) are an emerging technology that provide an opportunity to increase agricultural efficiency through remote sensing and automatic inferencing of field conditions. This paper examined the novel use of CNNs to identify two weeds, hair fescue and sheep sorrel, in images of wild blueberry fields. Commercial herbicide sprayers provide a uniform application of agrochemicals to manage patches of these weeds. Three object-detection and three image-classification CNNs were trained to identify hair fescue and sheep sorrel using images from 58 wild blueberry fields. The CNNs were trained using 1280x720 images and were tested at four different internal resolutions. The CNNs were retrained with progressively smaller training datasets ranging from 3780 to 472 images to determine the effect of dataset size on accuracy. YOLOv3-Tiny was the best object-detection CNN, detecting at least one target weed per image with F 1-scores of 0.97 for hair fescue and 0.90 for sheep sorrel at 1280 × 736 resolution. Darknet Reference was the most accurate image-classification CNN, classifying images containing hair fescue and sheep sorrel with F 1-scores of 0.96 and 0.95, respectively at 1280 × 736. MobileNetV2 achieved comparable results at the lowest resolution, 864 × 480, with F 1-scores of 0.95 for both weeds. Training dataset size had minimal effect on accuracy for all CNNs except Darknet Reference. This technology can be used in a smart sprayer to control target specific spray applications, reducing herbicide use. Future work will involve testing the CNNs for use on a smart sprayer and the development of an application to provide growers with field-specific information. Using CNNs to improve agricultural efficiency will create major cost-savings for wild blueberry producers.
Patrick Hennessy; Travis Esau; Aitazaz Farooque; Arnold Schumann; Qamar Zaman; Kenny Corscadden. Hair Fescue and Sheep Sorrel Identification Using Deep Learning in Wild Blueberry Production. Remote Sensing 2021, 13, 943 .
AMA StylePatrick Hennessy, Travis Esau, Aitazaz Farooque, Arnold Schumann, Qamar Zaman, Kenny Corscadden. Hair Fescue and Sheep Sorrel Identification Using Deep Learning in Wild Blueberry Production. Remote Sensing. 2021; 13 (5):943.
Chicago/Turabian StylePatrick Hennessy; Travis Esau; Aitazaz Farooque; Arnold Schumann; Qamar Zaman; Kenny Corscadden. 2021. "Hair Fescue and Sheep Sorrel Identification Using Deep Learning in Wild Blueberry Production." Remote Sensing 13, no. 5: 943.
Biochar produced from transforming bioresource waste can benefit sustainable agriculture and support circular bioeconomy. The objective of this study was to evaluate the effect of the application of biochar, produced from wheat straws, and a nitrification inhibitor, sourced from neem (Azadirachta indica), in combinition with the recommended synthetic fertilizer on soil properties, maize (Zea mays L.) plant growth characteristics, and maize grain yield and quality paramters. The nitrification inhibitor was used with the concentrations of 5 and 10 mL pot−1 (N1 and N2, respectively) with four levels of biochar (B0 = 0 g, B1 = 35 g, B2 = 70 g, B3 = 105 g, B4 = 140 g pot−1), one recommended nitrogen, phosphorous, and potassium syntactic fertilizer (250, 125, and 100 kg ha−1, respectively) treatment, and one control treatment. The results showed that the nitrification inhibitor enhanced crop growth while the application of biochar significantly improved soil fertility. The application of biochar significantly enhanced soil organic matter and soil nitrogen as compared with nitrogen–phosphorus–potassium treatment. The highest root length (65.43 cm) and root weight (50.25 g) were observed in the maize plants treated with B4 and N2 combinedly. The grain yield, total biomass production, protein content from biochar’s B4, and nitrogen–phosphorus–potassium treatments were not significantly different from each other. The application of 140 g biochar pot−1 (B4) with nitrification inhibitor (10 mL pot−1) resulted in higher crop yield and the highest protein contents in maize grains as compared to the control treatments. Therefore, the potential of biochar application in combination with nitrification inhibitor may be used as the best nutrient management practice after verifying these findings at a large-scale field study. Based on the experimental findings, the applied potential of the study treatments, and results of economic analysis, it can be said that biochar has an important role to play in the circular bioeconomy.
Farhat Abbas; Hafiz Hammad; Farhat Anwar; Aitazaz Farooque; Rashid Jawad; Hafiz Bakhat; Muhammad Naeem; Sajjad Ahmad; Saeed Qaisrani. Transforming a Valuable Bioresource to Biochar, Its Environmental Importance, and Potential Applications in Boosting Circular Bioeconomy While Promoting Sustainable Agriculture. Sustainability 2021, 13, 2599 .
AMA StyleFarhat Abbas, Hafiz Hammad, Farhat Anwar, Aitazaz Farooque, Rashid Jawad, Hafiz Bakhat, Muhammad Naeem, Sajjad Ahmad, Saeed Qaisrani. Transforming a Valuable Bioresource to Biochar, Its Environmental Importance, and Potential Applications in Boosting Circular Bioeconomy While Promoting Sustainable Agriculture. Sustainability. 2021; 13 (5):2599.
Chicago/Turabian StyleFarhat Abbas; Hafiz Hammad; Farhat Anwar; Aitazaz Farooque; Rashid Jawad; Hafiz Bakhat; Muhammad Naeem; Sajjad Ahmad; Saeed Qaisrani. 2021. "Transforming a Valuable Bioresource to Biochar, Its Environmental Importance, and Potential Applications in Boosting Circular Bioeconomy While Promoting Sustainable Agriculture." Sustainability 13, no. 5: 2599.
Assessment of Global Navigation Satellite Signal (GNSS) autosteering is a critical step in the progression towards full wild blueberry (vaccinium angustifolium) harvester automation. The objective of the study was to analyze John Deere’s universal Auto-Trac 300 autosteer, 4640 display, and Starfire 6000 receiver with both the SF1 and SF3 signal levels for their pass-to-pass accuracy as well as how they compared versus a manual harvester operator. Incorporation of GNSS autosteer in wild blueberry harvesting has never been assessed as the slow harvester travel speeds and small working width caused the implementation to be too challenging. The results of this study concluded that there were no significant differences in pass-to-pass accuracy based on travel speeds of 0.31 m s−1, 0.45 m s−1, and 0.58 m s−1 (p = 0.174). Comparing the signal levels showed significantly greater accuracy of the SF3 system (p < 0.001), which yielded an absolute mean pass-to-pass accuracy 22.7 mm better than SF1. Neither the SF1 nor SF3 signal levels were able to reach the levels of accuracy advertised by the manufacturer. That said, both signal levels performed better than a manual operator (p < 0.001). This result serves to support the idea that in the absence of skilled operators, an autosteer system can provide significant support for new operators. Further, an autosteer system can allow any operator to focus more of their attention on operating the harvester head and properly filling storage bins. This will lead to higher quality berries with less debris and spoilage. The results of this study are encouraging and represent a significant step towards full harvester automation for the wild blueberry crop.
Travis Esau; Craig MacEachern; Aitazaz Farooque; Qamar Zaman. Evaluation of Autosteer in Rough Terrain at Low Ground Speed for Commercial Wild Blueberry Harvesting. Agronomy 2021, 11, 384 .
AMA StyleTravis Esau, Craig MacEachern, Aitazaz Farooque, Qamar Zaman. Evaluation of Autosteer in Rough Terrain at Low Ground Speed for Commercial Wild Blueberry Harvesting. Agronomy. 2021; 11 (2):384.
Chicago/Turabian StyleTravis Esau; Craig MacEachern; Aitazaz Farooque; Qamar Zaman. 2021. "Evaluation of Autosteer in Rough Terrain at Low Ground Speed for Commercial Wild Blueberry Harvesting." Agronomy 11, no. 2: 384.
Excessive use of herbicides for weed control increases the cost of crop production and can lead to environmental degradation. An intelligent spraying system can apply agrochemicals on an as-needed basis by detecting and selectively targeting the weeds. The objective of this research was to investigate the feasibility of using deep convolutional neural networks (DCNNs) for detecting lamb’s quarters (Chenopodium album) in potato fields. Five potato fields were selected in Prince Edward Island (PEI) and New Brunswick (NB), Canada to collect images of spatially and temporally varied potato plants and lamb’s quarters. The image database included pictures, taken under varying growth stages of potato, outdoor light (clear, cloudy, and partly cloudy), and shadowy conditions. The images were trained for DCNN models, namely GoogLeNet, VGG-16, and EfficientNet to classify lamb’s quarters and potato plants. Performance of two frameworks, namely TensorFlow and PyTorch, were compared in training, testing, and during inferring the DCNNs. Results showed excellent performance of DCNNs in lamb’s quarters and potato plant classification (accuracy > 90%). However, the EfficientNet with PyTorch framework showed a maximum accuracy of (0.92–0.97) for every growth stage of the plants. Inference times of DCNNs were recorded using three graphics processing units (GPUs), namely Nvidia GeForce 930MX, Nvidia GeForce GTX1080 Ti, and Nvidia GeForce GTX1050. All the DCNNs performed better with PyTorch than TensorFlow frameworks. It was concluded that the trained models can be used in automation of the spraying systems for the site-specific application of agrochemicals for weed control in potato fields. Such precision agriculture technologies will ensure economically viable and environmentally safe potato cultivation.
Nazar Hussain; Aitazaz A. Farooque; Arnold W. Schumann; Farhat Abbas; Bishnu Acharya; Andrew McKenzie-Gopsill; Ryan Barrett; Hassan Afzaal; Qamar U. Zaman; Muhammad J.M. Cheema. Application of deep learning to detect Lamb’s quarters (Chenopodium album L.) in potato fields of Atlantic Canada. Computers and Electronics in Agriculture 2021, 182, 106040 .
AMA StyleNazar Hussain, Aitazaz A. Farooque, Arnold W. Schumann, Farhat Abbas, Bishnu Acharya, Andrew McKenzie-Gopsill, Ryan Barrett, Hassan Afzaal, Qamar U. Zaman, Muhammad J.M. Cheema. Application of deep learning to detect Lamb’s quarters (Chenopodium album L.) in potato fields of Atlantic Canada. Computers and Electronics in Agriculture. 2021; 182 ():106040.
Chicago/Turabian StyleNazar Hussain; Aitazaz A. Farooque; Arnold W. Schumann; Farhat Abbas; Bishnu Acharya; Andrew McKenzie-Gopsill; Ryan Barrett; Hassan Afzaal; Qamar U. Zaman; Muhammad J.M. Cheema. 2021. "Application of deep learning to detect Lamb’s quarters (Chenopodium album L.) in potato fields of Atlantic Canada." Computers and Electronics in Agriculture 182, no. : 106040.
This study evaluated the potential of using machine vision in combination with deep learning (DL) to identify the early blight disease in real-time for potato production systems. Four fields were selected to collect images (n = 5199) of healthy and diseased potato plants under variable lights and shadow effects. A database was constructed using DL to identify the disease infestation at different stages throughout the growing season. Three convolutional neural networks (CNNs), namely GoogleNet, VGGNet, and EfficientNet, were trained using the PyTorch framework. The disease images were classified into three classes (2-class, 4-class, and 6-class) for accurate disease identification at different growth stages. Results of 2-class CNNs for disease identification revealed the significantly better performance of EfficientNet and VGGNet when compared with the GoogleNet (FScore range: 0.84–0.98). Results of 4-Class CNNs indicated better performance of EfficientNet when compared with other CNNs (FScore range: 0.79–0.94). Results of 6-class CNNs showed similar results as 4-class, with EfficientNet performing the best. GoogleNet, VGGNet, and EfficientNet inference time values ranged from 6.8–8.3, 2.1–2.5, 5.95–6.53 frames per second, respectively, on a Dell Latitude 5580 using graphical processing unit (GPU) mode. Overall, the CNNs and DL frameworks used in this study accurately classified the early blight disease at different stages. Site-specific application of fungicides by accurately identifying the early blight infected plants has a strong potential to reduce agrochemicals use, improve the profitability of potato growers, and lower environmental risks (runoff of fungicides to water bodies).
Hassan Afzaal; Aitazaz Farooque; Arnold Schumann; Nazar Hussain; Andrew McKenzie-Gopsill; Travis Esau; Farhat Abbas; Bishnu Acharya. Detection of a Potato Disease (Early Blight) Using Artificial Intelligence. Remote Sensing 2021, 13, 411 .
AMA StyleHassan Afzaal, Aitazaz Farooque, Arnold Schumann, Nazar Hussain, Andrew McKenzie-Gopsill, Travis Esau, Farhat Abbas, Bishnu Acharya. Detection of a Potato Disease (Early Blight) Using Artificial Intelligence. Remote Sensing. 2021; 13 (3):411.
Chicago/Turabian StyleHassan Afzaal; Aitazaz Farooque; Arnold Schumann; Nazar Hussain; Andrew McKenzie-Gopsill; Travis Esau; Farhat Abbas; Bishnu Acharya. 2021. "Detection of a Potato Disease (Early Blight) Using Artificial Intelligence." Remote Sensing 13, no. 3: 411.
The uniform application (UA) of agrochemicals results in the over-application of harmful chemicals, increases crop input costs, and deteriorates the environment when compared with variable rate application (VA). A smart variable rate sprayer (SVRS) was designed, developed, and tested using deep learning (DL) for VA application of agrochemicals. Real-time testing of the SVRS took place for detecting and spraying and/or skipping lambsquarters weed and early blight infected and healthy potato plants. About 24,000 images were collected from potato fields in Prince Edward Island and New Brunswick under varying sunny, cloudy, and partly cloudy conditions and processed/trained using YOLOv3 and tiny-YOLOv3 models. Due to faster performance, the tiny-YOLOv3 was chosen to deploy in SVRS. A laboratory experiment was designed under factorial arrangements, where the two spraying techniques (UA and VA) and the three weather conditions (cloudy, partly cloudy, and sunny) were the two independent variables with spray volume consumption as a response variable. The experimental treatments had six repetitions in a 2 × 3 factorial design. Results of the two-way ANOVA showed a significant effect of spraying application techniques on volume consumption of spraying liquid (p-value < 0.05). There was no significant effect of weather conditions and interactions between the two independent variables on volume consumption during weeds and simulated diseased plant detection experiments (p-value > 0.05). The SVRS was able to save 42 and 43% spraying liquid during weeds and simulated diseased plant detection experiments, respectively. Water sensitive papers’ analysis showed the applicability of SVRS for VA with >40% savings of spraying liquid by SVRS when compared with UA. Field applications of this technique would reduce the crop input costs and the environmental risks in conditions (weed and disease) like experimental testing.
Nazar Hussain; Aitazaz Farooque; Arnold Schumann; Andrew McKenzie-Gopsill; Travis Esau; Farhat Abbas; Bishnu Acharya; Qamar Zaman. Design and Development of a Smart Variable Rate Sprayer Using Deep Learning. Remote Sensing 2020, 12, 4091 .
AMA StyleNazar Hussain, Aitazaz Farooque, Arnold Schumann, Andrew McKenzie-Gopsill, Travis Esau, Farhat Abbas, Bishnu Acharya, Qamar Zaman. Design and Development of a Smart Variable Rate Sprayer Using Deep Learning. Remote Sensing. 2020; 12 (24):4091.
Chicago/Turabian StyleNazar Hussain; Aitazaz Farooque; Arnold Schumann; Andrew McKenzie-Gopsill; Travis Esau; Farhat Abbas; Bishnu Acharya; Qamar Zaman. 2020. "Design and Development of a Smart Variable Rate Sprayer Using Deep Learning." Remote Sensing 12, no. 24: 4091.
The delineation of management zones (MZs) has been suggested as a solution to mitigate adverse impacts of soil variability on potato tuber yield. This study quantified the spatial patterns of variability in soil and crop properties to delineate MZs for site-specific soil fertility characterization of potato fields through proximal sensing of fields. Grid sampling strategy was adopted to collect soil and crop data from two potato fields in Prince Edward Island (PEI). DUALEM-2 sensor, Time Domain Reflectometry (TDR-300), GreenSeeker were used to collect soil ground conductivity parameter horizontal coplanar geometry (HCP), soil moisture content (θ), and normalized difference vegetative index (NDVI), respectively. Soil organic matter (SOM), soil pH, phosphorous (P), potash (K), iron (Fe), lime index (LI), and cation exchange capacity (CEC) were determined from soil samples collected from each grid. Stepwise regression shortlisted the major properties of soil and crop that explained 71 to 86% of within-field variability. The cluster analysis grouped the soil and crop data into three zones, termed as excellent, medium, and poor at a 40% similarity level. The coefficient of variation and the interpolated maps characterized least to moderate variability of soil fertility parameters, except for HCP and K that were highly variable. The results of multiple means comparison indicated that the tuber yield and HCP were significantly different in all MZs. The significant relationship between HCP and yield suggested that the ground conductivity data could be used to develop MZs for site-specific fertilization in potato fields similar to those used in this study.
Humna Khan; Aitazaz A. Farooque; Bishnu Acharya; Farhat Abbas; Travis J. Esau; Qamar U. Zaman. Delineation of Management Zones for Site-Specific Information about Soil Fertility Characteristics through Proximal Sensing of Potato Fields. Agronomy 2020, 10, 1854 .
AMA StyleHumna Khan, Aitazaz A. Farooque, Bishnu Acharya, Farhat Abbas, Travis J. Esau, Qamar U. Zaman. Delineation of Management Zones for Site-Specific Information about Soil Fertility Characteristics through Proximal Sensing of Potato Fields. Agronomy. 2020; 10 (12):1854.
Chicago/Turabian StyleHumna Khan; Aitazaz A. Farooque; Bishnu Acharya; Farhat Abbas; Travis J. Esau; Qamar U. Zaman. 2020. "Delineation of Management Zones for Site-Specific Information about Soil Fertility Characteristics through Proximal Sensing of Potato Fields." Agronomy 10, no. 12: 1854.
Sufficient production, consistent food supply, and environmental protection in urban +settings are major global concerns for future sustainable cities. Currently, sustainable food supply is under intense pressure due to exponential population growth, expanding urban dwellings, climate change, and limited natural resources. The recent novel coronavirus 2019 (COVID-19) pandemic crisis has impacted sustainable fresh food supply, and has disrupted the food supply chain and prices significantly. Under these circumstances, urban horticulture and crop cultivation have emerged as potential ways to expand to new locations through urban green infrastructure. Therefore, the objective of this study is to review the salient features of contemporary urban horticulture, in addition to illustrating traditional and innovative developments occurring in urban environments. Current urban cropping systems, such as home gardening, community gardens, edible landscape, and indoor planting systems, can be enhanced with new techniques, such as vertical gardening, hydroponics, aeroponics, aquaponics, and rooftop gardening. These modern techniques are ecofriendly, energy- saving, and promise food security through steady supplies of fresh fruits and vegetables to urban neighborhoods. There is a need, in this modern era, to integrate information technology tools in urban horticulture, which could help in maintaining consistent food supply during (and after) a pandemic, as well as make agriculture more sustainable.
Muhammad Khan; Muhammad Akram; Rhonda Janke; Rashad Qadri; Abdullah Al-Sadi; Aitazaz Farooque. Urban Horticulture for Food Secure Cities through and beyond COVID-19. Sustainability 2020, 12, 9592 .
AMA StyleMuhammad Khan, Muhammad Akram, Rhonda Janke, Rashad Qadri, Abdullah Al-Sadi, Aitazaz Farooque. Urban Horticulture for Food Secure Cities through and beyond COVID-19. Sustainability. 2020; 12 (22):9592.
Chicago/Turabian StyleMuhammad Khan; Muhammad Akram; Rhonda Janke; Rashad Qadri; Abdullah Al-Sadi; Aitazaz Farooque. 2020. "Urban Horticulture for Food Secure Cities through and beyond COVID-19." Sustainability 12, no. 22: 9592.
The original version of this paper was published with error. Corresponding author requested to make a necessary correction in the spelling for the last author. The correct name is “Travis Esau” instead of “Travis Easu”.
Aitazaz A. Farooque; Qamar Zaman; Farhat Abbas; Hafiz Mohkum Hammad; Bishnu Acharya; Travis Esau. Correction to: How can potatoes be smartly cultivated with biochar as a soil nutrient amendment technique in Atlantic Canada? Arabian Journal of Geosciences 2020, 13, 1 -1.
AMA StyleAitazaz A. Farooque, Qamar Zaman, Farhat Abbas, Hafiz Mohkum Hammad, Bishnu Acharya, Travis Esau. Correction to: How can potatoes be smartly cultivated with biochar as a soil nutrient amendment technique in Atlantic Canada? Arabian Journal of Geosciences. 2020; 13 (18):1-1.
Chicago/Turabian StyleAitazaz A. Farooque; Qamar Zaman; Farhat Abbas; Hafiz Mohkum Hammad; Bishnu Acharya; Travis Esau. 2020. "Correction to: How can potatoes be smartly cultivated with biochar as a soil nutrient amendment technique in Atlantic Canada?" Arabian Journal of Geosciences 13, no. 18: 1-1.
Proximal sensing techniques can potentially survey soil and crop variables responsible for variations in crop yield. The full potential of these precision agriculture technologies may be exploited in combination with innovative methods of data processing such as machine learning (ML) algorithms for the extraction of useful information responsible for controlling crop yield. Four ML algorithms, namely linear regression (LR), elastic net (EN), k-nearest neighbor (k-NN), and support vector regression (SVR), were used to predict potato (Solanum tuberosum) tuber yield from data of soil and crop properties collected through proximal sensing. Six fields in Atlantic Canada including three fields in Prince Edward Island (PE) and three fields in New Brunswick (NB) were sampled, over two (2017 and 2018) growing seasons, for soil electrical conductivity, soil moisture content, soil slope, normalized-difference vegetative index (NDVI), and soil chemistry. Data were collected from 39–40 30 × 30 m2 locations in each field, four times throughout the growing season, and yield samples were collected manually at the end of the growing season. Four datasets, namely PE-2017, PE-2018, NB-2017, and NB-2018, were then formed by combing data points from three fields to represent the province data for the respective years. Modeling techniques were employed to generate yield predictions assessed with different statistical parameters. The SVR models outperformed all other models for NB-2017, NB-2018, PE-2017, and PE-2018 dataset with RMSE of 5.97, 4.62, 6.60, and 6.17 t/ha, respectively. The performance of k-NN remained poor in three out of four datasets, namely NB-2017, NB-2018, and PE-2017 with RMSE of 6.93, 5.23, and 6.91 t/ha, respectively. The study also showed that large datasets are required to generate useful results using either model. This information is needed for creating site-specific management zones for potatoes, which form a significant component for food security initiatives across the globe.
Farhat Abbas; Hassan Afzaal; Aitazaz A. Farooque; Skylar Tang. Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms. Agronomy 2020, 10, 1046 .
AMA StyleFarhat Abbas, Hassan Afzaal, Aitazaz A. Farooque, Skylar Tang. Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms. Agronomy. 2020; 10 (7):1046.
Chicago/Turabian StyleFarhat Abbas; Hassan Afzaal; Aitazaz A. Farooque; Skylar Tang. 2020. "Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms." Agronomy 10, no. 7: 1046.
Agricultural management practices are responsible for almost two-thirds of the variations in potato tuber yield. In order to answer the research question about the remaining variability of the tuber yield, we hypothesized that climate extremes partly explain the missing component of variations of the tuber yield. Therefore, this research attempts to bridge this knowledge gap in order to generate a knowledge base for future strategies. A climate extreme dataset of the Prince Edward Island (PEI) was computed by averaging the data of five meteorological stations. In detail, changing patterns of 20 climate extreme indices were computed with ClimPACT2 software for 30 years (1989-2018) data of PEI. Statistical significance of the trends and their slope values were determined with the Mann-Kendall test and Sen’s slope estimates, respectively. Average of daily mean temperature (TMm), mean daily minimum temperature (TNm) and the occurrence of continuous dry days (CDD), significantly increased by 0.77 °C, 1.17 °C and 3.33 days., respectively, during the potato growing seasons (May-October) of the past three decades. For this period daily temperature range (DTR), frost days (FD), cold days (TX10p), cold nights (TN10p) and warmest days (TXx) showed decreasing trends of −1.01 °C, −3.75 days, −5.67 days, −11.40 nights, and −2.00 days, respectively. The principal component analysis showed that DTR, TXx, CDD, and TNm were the main factors affecting seasonal variations of tuber yield. The multiple regression model attributed ~39% of tuber yield variance to DTR, TXx, CDD, and TNm. However, these indices explained individually 21%, 19%, 16%, and 4% variation to the tuber yield, respectively. The remaining variation in the tuber yield explained by other yield affecting factors. The information generated from this study can be used for future planning about agricultural management strategies in the Island, for example, the provision of water resources for supplemental irrigation of crops during dry months.
Junaid Maqsood; Aitazaz A. Farooque; Xander Wang; Farhat Abbas; Bishnu Acharya; Hassan Afzaal. Contribution of Climate Extremes to Variation in Potato Tuber Yield in Prince Edward Island. Sustainability 2020, 12, 1 .
AMA StyleJunaid Maqsood, Aitazaz A. Farooque, Xander Wang, Farhat Abbas, Bishnu Acharya, Hassan Afzaal. Contribution of Climate Extremes to Variation in Potato Tuber Yield in Prince Edward Island. Sustainability. 2020; 12 (12):1.
Chicago/Turabian StyleJunaid Maqsood; Aitazaz A. Farooque; Xander Wang; Farhat Abbas; Bishnu Acharya; Hassan Afzaal. 2020. "Contribution of Climate Extremes to Variation in Potato Tuber Yield in Prince Edward Island." Sustainability 12, no. 12: 1.
Mechanical harvesting of wild blueberries remains the most cost-effective means for harvesting the crop. Harvesting of wild blueberries is heavily reliant on operator skill and full automation of the harvester will rely on precise and accurate determination of the picking reel’s height. This study looked at developing a control system which would provide feedback on harvester picking reel height on up to five harvester heads. Additionally, the control system looked at implementing three quality of life improvements for operators, operating multiple heads until the point when full automation is achieved. These three functions were a tandem movement function, a baseline function, and a set-to-one function. Each of these functions were evaluated for their precision and accuracy and returned absolute mean discrepancies of 3.10, 2.20, and 2.50 mm respectively. Both electric and hydraulic actuators were evaluated for their effectiveness in this system however, the electric actuator was simply too slow to be deemed viable for the commercial harvesters. To achieve the full 203.2 mm stroke required by the harvester head, the electric actuator required 13.96 s while the hydraulic actuator required only 2.30 s under the same load.
Travis J. Esau; Craig B. MacEachern; Qamar U. Zaman; And Aitazaz A. Farooque. Development and Evaluation of a Closed-Loop Control System for Automation of a Mechanical Wild Blueberry Harvester’s Picking Reel. AgriEngineering 2020, 2, 322 -335.
AMA StyleTravis J. Esau, Craig B. MacEachern, Qamar U. Zaman, And Aitazaz A. Farooque. Development and Evaluation of a Closed-Loop Control System for Automation of a Mechanical Wild Blueberry Harvester’s Picking Reel. AgriEngineering. 2020; 2 (2):322-335.
Chicago/Turabian StyleTravis J. Esau; Craig B. MacEachern; Qamar U. Zaman; And Aitazaz A. Farooque. 2020. "Development and Evaluation of a Closed-Loop Control System for Automation of a Mechanical Wild Blueberry Harvester’s Picking Reel." AgriEngineering 2, no. 2: 322-335.
The question if biochar is a suitable soil nutrient amendment for potato cultivation in the Atlantic Canada is yet to be answered. The objective of this study was to answer this question. Three replicates of twelve lysimeters, each 8000 cm2, were packed with an Atlantic Canada representative soil to cultivate potatoes with four treatments of soil amendments (T1 = control [no added nutrients], T2 = B [biochar], T3 = F [synthetic fertilizer @ recommended NPK], and T4 = B + F [biochar + recommended NPK]) under a completely randomized block design with factorial arrangements. Chemical analyses of soils were conducted for physical, hydrological, and chemical (including concentration of macro- and micro-nutrients) prior to and after the completion experiments to evaluate soil fertility and its resulting effects on crop yield. The biochar amendment improved soil micro- and macro-nutrients. Soil organic matter, pH, and cation exchange capacity (ECE) significantly increased by application of biochar. The maximum potato yield of 30,467.4 kg h−1 was achieved by the combined application of biochar and synthetic fertilizer as this combination resulted in the maximum net benefit ($4433.98 ha−1) in comparison with control treatment that had net loss of $– 2621.49 ha−1. It is therefore concluded that biochar amendment of soils resembling to that of the Atlantic Canada representative soil used in this study, with a mix of recommended NPK for, can formulate a smart precision farming nutrient management technique for this region subject to the field trials and replicate experimental treatments for more than three times.
Aitazaz A. Farooque; Qamar Zaman; Farhat Abbas; Hafiz Mohkum Hammad; Bishnu Acharya; Travis Easu. How can potatoes be smartly cultivated with biochar as a soil nutrient amendment technique in Atlantic Canada? Arabian Journal of Geosciences 2020, 13, 1 -9.
AMA StyleAitazaz A. Farooque, Qamar Zaman, Farhat Abbas, Hafiz Mohkum Hammad, Bishnu Acharya, Travis Easu. How can potatoes be smartly cultivated with biochar as a soil nutrient amendment technique in Atlantic Canada? Arabian Journal of Geosciences. 2020; 13 (9):1-9.
Chicago/Turabian StyleAitazaz A. Farooque; Qamar Zaman; Farhat Abbas; Hafiz Mohkum Hammad; Bishnu Acharya; Travis Easu. 2020. "How can potatoes be smartly cultivated with biochar as a soil nutrient amendment technique in Atlantic Canada?" Arabian Journal of Geosciences 13, no. 9: 1-9.
Climate change induced uneven patterns of rainfall emphasize the use of supplemental irrigation in rainfed agriculture. The Penman–Monteith method was used to calculate supplemental irrigation for water budgeting of a potato crop in Prince Edward Island, Canada. Cumulative gaps between rainfall and crop evapotranspiration (ETc) during August and September of the study years were due to high crop coefficient factor, justifying the need for supplemental irrigation. Pressurized irrigation systems, including sprinklers, fertigation, and drip irrigation were installed, to evaluate the impact of scheduled supplemental irrigation in offsetting deficits in irrigation water requirements in comparison with conventional practice of rainfed cultivation (control). A two-way ANOVA examined the effect of irrigation methods and year on potato tuber yield, water productivity, tuber quality, and payout. Sprinkler and fertigation systems performed better than drip and control treatments. In terms of payout returns and potato tuber quality (percentage of marketable potatoes), the sprinkler treatment performed significantly better than the other treatments. However, for water productivity, fertigation treatment performed significantly better than control and sprinkler treatments during both years. The use of supplemental irrigation is recommended for profitable cultivation of potatoes in soil, agricultural, and environmental conditions resembling to those of Prince Edward Island.
Hassan Afzaal; Aitazaz A. Farooque; Farhat Abbas; Bishnu Acharya; Travis Esau. Precision Irrigation Strategies for Sustainable Water Budgeting of Potato Crop in Prince Edward Island. Sustainability 2020, 12, 2419 .
AMA StyleHassan Afzaal, Aitazaz A. Farooque, Farhat Abbas, Bishnu Acharya, Travis Esau. Precision Irrigation Strategies for Sustainable Water Budgeting of Potato Crop in Prince Edward Island. Sustainability. 2020; 12 (6):2419.
Chicago/Turabian StyleHassan Afzaal; Aitazaz A. Farooque; Farhat Abbas; Bishnu Acharya; Travis Esau. 2020. "Precision Irrigation Strategies for Sustainable Water Budgeting of Potato Crop in Prince Edward Island." Sustainability 12, no. 6: 2419.