Ahmed Asaad Zaeen
@scopus.com
Remote Sensing Unit, College of Science, University of Baghdad
Remote Sensing Unit, College of Science, University of Baghdad
PhD in Agricultural Sciences-Remote Sensing from the University of Maine, USA (2020), supervised by Dr. Lakesh K. Sharma.
2-M.Sc. in agricultural sciences-remote sensing, soil and water resources from University ofBaghdad, Iraq, (2008), supervised by Dr. Ahmed S. Muhaimed.
3- B.Sc. in agriculture sciences, soil science & water resources from the University of Baghdad,Iraq (2005).
EDUCATION
PhD in Agricultural Sciences-Remote Sensing from the University of Maine, USA (2020), supervised by Dr. Lakesh K. Sharma.
2-M.Sc. in agricultural sciences-remote sensing, soil and water resources from University ofBaghdad, Iraq, (2008), supervised by Dr. Ahmed S. Muhaimed.
3- B.Sc. in agriculture sciences, soil science & water resources from the University of Baghdad,Iraq (2005).
RESEARCH, TEACHING, or OTHER INTERESTS
Soil Science, Plant Science, Agronomy and Crop Science
Scopus Publications
Scopus Publications
Khalid H. Abbas Al-Aarajy, Ahmed A. Zaeen, and Khaleel I. Abood
Association for Information Communication Technology Education and Science (UIKTEN)
Assessing the accuracy of classification algorithms is paramount as it provides insights into reliability and effectiveness in solving real-world problems. Accuracy examination is essential in any remote sensing-based classification practice, given that classification maps consistently include misclassified pixels and classification misconceptions. In this study, two imaginary satellites for Duhok province, Iraq, were captured at regular intervals, and the photos were analyzed using spatial analysis tools to provide supervised classifications. Some processes were conducted to enhance the categorization, like smoothing. The classification results indicate that Duhok province is divided into four classes: vegetation cover, buildings, water bodies, and bare lands. During 2013-2022, vegetation cover increased from 63% in 2013 to 66% in 2022; buildings roughly increased by 1% to 3% yearly; water bodies showed a decrease of 2% to 1%; the amount of unoccupied land showed a decrease from 34% to 30%. Therefore, the classification accuracy was assessed using the approach of comparison with field data; the classification accuracy was about 85%.
Laith A. Jawad, Ahmed A. Zaeen, and Tariq Z. Hamood
University of Baghdad College of Science
The extraction, study, and accurate interpretation of the morphology database of a basin are the basic blocks for building a valid geomorphological understanding of this basin. In this work, a new approach is presented which is to use three different GIS based methods to extract databases with specific geographical information and then use the concept of information intersection to make a realistic geomorphological perspective for the study area. In the first method, data integration of remote sensing images from Google Map and SRTM DEM images were used to identify Horan basin borders. In the second method, the principle of data integration was represented by extracting the quantitative values of the morphometric characteristics that were affected by the geomorphological condition of the studied basin, such as the shape factor, circulation factor, and relief ratio, then eliciting an optimal conception of the geomorphological condition of the basin from the meanings and connotations of these combined transactions. The third method used the same principle by taking the optimal inferences from the integration of the interpretation of the values of the Hypsometry integration coefficients for each area in the basin separately with the integration value of the drawing curve for the relative heights of the basin areas with their relative areas. It was found, from the values of the coefficients, that the areas (A, B, C, D, and F) were still in the early stages of youth. Whereas the E region was in the maturity stage and the G region was in the monadnock stage of the geomorphological cycle. As for the integral value of the curve, it indicated 48. 559 % erosion from the surface of the basin only, and that its boundaries were subject to change and widening.
Ahmed Asaad Zaeen, Laith Aziz Jawad, Lakesh K. Sharma, and Tareq Zaid Hamood
University of Baghdad College of Science
The increased food requirement puts intense pressure on the agriculture community to grow more from the same resources resulting in people leaving the farming business. This happened not exclusively due to the industrial pressure to produce more but to the lack of technology adoption among growers. The use of the sensor in agriculture is not new, but its adoption among agriculture producers is a challenge for industry and scientists. This study aimed to determine sensors used in agricultural fields with challenges and prospects. The study found that sensors have successfully been used at the industry level with highly skilled labor; however, their adoption is challenging in rural agriculture systems due to the lack of a support system. The study found that the sensors used in predicting crop parameters, yield, quality, insect attacks, leaf damage, and several plants are crucial parameters to study. Sensors, particularly ground-based active optical sensors, have performed well while developing algorithms where soil parameters, environmental factors, and sensors have successfully predicted crop yield and quality.
Ahmed Asaad Zaeen, Ruaa Muhammed Dhedan, and Lakesh K. Sharma
University of Baghdad College of Science
Nowadays, a strong relationship between the agriculture sectors and digital technologies is really interesting. The article describes how recent intelligent technologies can improve agricultural fields. Mobile applications are software programs created on smartphones, tablets, and computers. Agricultural fields mainly represent the pillar of the economy and the business sector that fulfills the world's food requirements. The United States has a valuable rank in potato production, which depends on this production economically. Nevertheless, so many insects affect potato yield production quantitatively and qualitatively. So, a smartphone App was created to help potato growers diagnose insects that directly attack potato crops and treat them. The created App focuses on a list of the common insects that attack potato crops in Maine State. App Inventor Platform, run by the Massachusetts Institute of Technology (MIT), was used to develop the application. Insect images and insecticides information were collected from the Cooperative Extension Department at Presque Isle City, Aroostook County, Maine, USA. The App provides essential details regarding insect types, life cycles, where they are coming from, and the time of attacking the plants. The App includes a list of effective insecticides that control insects. The App also provides helpful instructions concerning trade names, dose per acre of insecticides, and whether it should be applied to soil or plant leaves. Money and time are saved by applying this App since farmers do not need to spend time collecting samples and bringing them to the lab.
Ahmed. A. Z., L. Sharma, S. Bali, A. Buzza, and A. Alyokhin
University of Baghdad - College of Agriculture
This study was performed to determine whether active optical sensors could develop an algorithm for N recommendation for the potato crop (Solanum tuberosum L.). The experiment was conducted in Maine State, (USA) during the growing season of 2018-2019. Six N rates (0-280 kg ha-1) were applied on eleven locations under a randomized complete block design (RCBD), with four replications. Data of normalized difference vegetation index-(NDVI) were collected via active sensors, GreenSeeker-(GS), and Crop Circle-(CC). Sensors measurements collected at the 20th of the leaf stage were significantly associated with tuber yield, where the exponential model exhibited a better fit for the regression curve. Conventionally, 168 kg N ha-1 produced the maximum potato yield. The N rate computed based on in-season sensors reading reduced by about 12-14% from the total N rate that growers use to apply based on the conventional approach. Studying potato cultivars separately in the same soil properties can improve the algorithm accurately.
Lakesh K. Sharma, Ahmed Zaeen, and Sukhwinder Bali
Elsevier
Ahmed Jasim, Ahmed Zaeen, Lakesh K. Sharma, Sukhwinder K. Bali, Chunzeng Wang, Aaron Buzza, and Andrei Alyokhin
Agriculture (Switzerland) MDPI AG
Applications of remote sensing are important in improving potato production through the broader adoption of precision agriculture. This technology could be useful in decreasing the potential contamination of soil and water due to the over-fertilization of agriculture crops. The objective of this study was to assess the utility of active sensors (Crop Circle™, Holland Scientific, Inc., Lincoln, NE, USA and GreenSeeker™, Trimble Navigation Limited, Sunnyvale, CA, USA) and passive sensors (multispectral imaging with Unmanned Arial Vehicles (UAVs)) to predict total potato yield and phosphorus (P) uptake. The experimental design was a randomized complete block with four replications and six P treatments, ranging from 0 to 280 kg P ha−1, as triple superphosphate (46% P2O5). Vegetation indices (VIs) and plant pigment levels were calculated at various time points during the potato growth cycle, correlated with total potato yields and P uptake by the stepwise fitting of multiple linear regression models. Data generated by Crop Circle™ and GreenSeeker™ had a low predictive value of potato yields, especially early in the season. Crop Circle™ performed better than GreenSeeker™ in predicting plant P uptake. In contrast, the passive sensor data provided good estimates of total yields early in the season but had a poor correlation with P uptake. The combined use of active and passive sensors presents an opportunity for better P management in potatoes.
Ahmed Jasim, Lakesh K. Sharma, Ahmed Zaeen, Sukhwinder K. Bali, Aaron Buzza, and Andrei Alyokhin
MDPI AG
Phosphorus (P) is an element that is potatoes require in large amounts. Soil pH is a crucial factor impacting phosphorus availability in potato production. This study was conducted to evaluate the influence of P application rates on the P efficiency for tuber yield, specific gravity, and P uptake. Additionally, the relationship between soil pH and total potato tuber yield was determined. Six rates of P fertilization (0–280 kg P ha−1) were applied at twelve different sites across Northern Maine. Yield parameters were not responsive to P application rates. However, regression analysis showed that soil pH was significantly correlated with total potato tuber yield(R2 = 0.38). Sites with soil pH values < 6 had total tuber yields, marketable tuber yields, tuber numbers per plant, and total tuber mean weights that were all higher than these same parameters at sites with soil pH ≥ 6. All sites with soil pH< 6 showed a highly correlated relationship between P uptake and petiole dry weight (R2 = 0.76). The P application rate of 56 kg P ha−1 was the best at sites with a soil pH < 6, but 0–56 kg P ha−1 was the best at sites with soil pH ≥ 6.
Ahmed A. Zaeen, Lakesh K. Sharma, Ahmed Jasim, Sukhwinder Bali, Aaron Buzza, and Andrei Alyokhin
Wiley
AbstractUndesirable growth of potato (Solanum tuberosum L.) crop under an excessive N fertilizer application is the main obstacle presently. This research was conducted to investigate the response of different potato cultivars; Russet Burbank, Shepody, and Superior, and its qualitative characteristics under a series of N rates. Six rates of N fertilization (0–280 kg ha−1) were applied on 11 sites in a randomized complete block design, with four replications. Sites with ≥30 g kg−1 of soil organic matter (OM) produced total tuber yield, marketable yield, and tuber weight per plant 39.5, 45.2, and 54.9%, respectively, higher than sites with ≤30 g kg−1 of OM. Tubers specific gravity increased by 0.18% in the sites with ≥30 g kg−1 of OM. The total tuber yield for the three cultivars was maximized at 168 kg N ha−1. Marketable specific gravity, starch, and dry matter content were achieved by applying 168 and 112 kg N ha−1 at ≤30 and ≥30 g kg−1 of OM sites, respectively. Russet Burbank produced a higher yield than Shepody and Superior cultivars significantly, but there was no significant difference among them regarding specific gravity. Excessive N application (>168 kg ha−1) decreased potato tuber production and quality.
Ahmed A. Zaeen, Lakesh Sharma, Ahmed Jasim, Sukhwinder Bali, Aaron Buzza, and Andrei Alyokhin
Wiley
AbstractCrop yield prediction is a critical measurement, especially in the time when parts of the world are suffering from farming issues. Yield forecasting gives an alert regarding economic trading, food production monitoring, and global food security. This research was conducted to investigate whether active optical sensors could be utilized for potato (Solanum tuberosum L.) yield prediction at the mid.le of the growing season. Three potato cultivars (Russet Burbank, Superior, and Shepody) were planted and six rates of N (0, 56, 112, 168, 224, and 280 kg ha−1), ammonium sulfate, which was replaced by ammonium nitrate in the 2nd year, were applied on 11 sites in a randomized complete block design, with four replications. Normalized difference vegetation index (NDVI) and chlorophyll index (CI) measurements were obtained weekly from the active optical sensors, GreenSeeker (GS) and Crop Circle (CC). The 168 kg N ha−1 produced the maximum potato yield. Indices measurements obtained at the 16th and 20th leaf growth stages were significantly correlated with tuber yield. Multiple regression analysis (potato yield as a dependent variable and vegetation indices, NDVI and CI, as independent variables) could make a remarkable improvement to the accuracy of the prediction model and increase the determination coefficient. The exponential and linear models showed a better fit of the data. Soil organic matter content increased the yield significantly but did not affect the prediction models. The 18th and 20th leaf growth stages are the best time to use the sensors for yield prediction.
L.K. Sharma, S.K. Bali, A.A. Zaeen, P. Baldwin, and D.W. Franzen
Wiley
Optical sensors are commonly used by the researchers to improve yield estimated in commercial crops. This study was carried out in two states in two different crops, corn and potatoes, 2011–2013 and 2017, respectively. The objectives of the study were to evaluate ground based optical sensors to predict yield potential across multiple locations, soils types, cultivation systems, and rainfall differences. Ground‐based active optical sensors (GBAOS) have been successfully used in agriculture to predict crop yield potential (YP) early in the season and to improvise N rates for optimal crop yield. However, the models were found weak or inconsistent due to environmental variation especially rainfall. The objectives of the study were to evaluate if GBAOS could predict YP across multiple locations, soil types, cultivation systems, and rainfall differences. This study was carried from 2011 to 2013 on corn (Zea mays L.) in North Dakota, and in 2017 in potatoes in Maine. Six N rates were used on 50 sites in North Dakota and 12 N rates on two sites, one dryland and one irrigated, in Maine. Two active GBAOS used for this study were GreenSeeker and Holland Scientific Crop Circle Sensor ACS 470 (HSCCACS‐470) and 430 (HSCCACS‐430). Rainfall data, with or without including crop height, improved the YP models in term of reliability and consistency. The polynomial model was relatively better compared to the exponential model. A significant difference in the relationship between sensor reading multiplied by rainfall data and crop yield was observed in terms of soil type, clay and medium textured, and cultivation system, conventional and no‐till, respectively, in the North Dakota corn study. The two potato sites in Maine, irrigated and dryland, performed differently in terms of total yield and rainfall data helped to improve sensor YP models. In conclusion, this study strongly advocates the use of rainfall data while using sensor‐based N calculator algorithms.
Lakesh Sharma, Sukhwinder Bali, and Ahmed Zaeen
MDPI AG
Recent phosphorus (P) pollution in the United States, mainly in Maine, has raised some severe concerns over the use of P fertilizer application rates in agriculture. Phosphorus is the second most limiting nutrient after nitrogen and has damaging impacts on crop yield if found to be deficient. Therefore, farmers tend to apply more P than is required to satisfy any P loss after its application at planting. Several important questions were raised in this study to improve P efficiency and reduce its pollution. The objective of this study was to find potential reasons for P pollution in water bodies despite a decrease in potato acreage. Historically, the potato was found to be responsible for P water contamination due to its high P sensitivity and low P removal (25–30 kg ha−1) from the soil. Despite University of Maine recommended rate of 56 kg ha−1 P, if soil tests reveal that P is below 50 kg ha−1, growers tend to apply P fertilizer at the rate of 182 kg ha−1 to compensate for any loss. The second key reason for excessive P application is its tendency to get fixed by aluminum (Al) in the soil. Soil sampling data from UMaine Soil Testing Laboratory confirmed that in Maine reactive Al levels have remained high over the last ten years and are increasing further. Likewise, P application to non-responsive sites, soil variability, pH change, and soil testing methods were found to be other possible reasons that might have led to increases in soil P levels resulting in P erosion to water streams.