Tofael Ahamed

Bio: Tofael Ahamed is an academic researcher from University of Tsukuba. The author has contributed to research in topics: Normalized Difference Vegetation Index & Computer science. The author has an hindex of 14, co-authored 86 publications receiving 657 citations. Previous affiliations of Tofael Ahamed include Bangabandhu Sheikh Mujibur Rahman Agricultural University & University of Illinois at Urbana–Champaign.

A review of remote sensing methods for biomass feedstock production.

Abstract: Monitoring and maximization of bioenergy yield from biomass feedstock has recently become a critically important goal for researchers Remote sensing represents a potential method to monitor and estimate biomass so as to increase biomass feedstock production from energy crops This paper reviews the biophysical properties of biomass and remote sensing methods for monitoring energy crops for site-specific management While several research studies have addressed the agronomic dimensions of this approach, more research is required on perennial energy crops in order to maximize the yield of biomass feedstock Assessment of established methods could lead to a new strategy to monitor energy crops for the adoption of site-specific management in biomass feedstock production In this article, satellite, aerial and ground-based remote sensing’s were reviewed and focused on the spatial and temporal resolutions of imagery to adopt for site-specific management We have concluded that the biomass yield prediction, the ground-based sensing is the most suitable to establish the calibration model and reference for aerial and satellite remote sensing The aerial and satellite remote sensing are required for wide converge of planning and policy implementations of biomass feedstock production systems

Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT

Abstract: This study aimed to produce a robust real-time pear fruit counter for mobile applications using only RGB data, the variants of the state-of-the-art object detection model YOLOv4, and the multiple object-tracking algorithm Deep SORT. This study also provided a systematic and pragmatic methodology for choosing the most suitable model for a desired application in agricultural sciences. In terms of accuracy, YOLOv4-CSP was observed as the optimal model, with an AP@0.50 of 98%. In terms of speed and computational cost, YOLOv4-tiny was found to be the ideal model, with a speed of more than 50 FPS and FLOPS of 6.8–14.5. If considering the balance in terms of accuracy, speed and computational cost, YOLOv4 was found to be most suitable and had the highest accuracy metrics while satisfying a real time speed of greater than or equal to 24 FPS. Between the two methods of counting with Deep SORT, the unique ID method was found to be more reliable, with an F1count of 87.85%. This was because YOLOv4 had a very low false negative in detecting pear fruits. The ROI line is more reliable because of its more restrictive nature, but due to flickering in detection it was not able to count some pears despite their being detected.

Land suitability analysis for maize production in Indonesia using satellite remote sensing and GIS-based multicriteria decision support system

Abstract: Maize is one of the potential crops can help in regional food production with self-sufficiency of foods in the drought prone areas of East Java in Indonesia. The purpose of this research is to determine the lands that are suitable for sustainable maize production in some selected areas of East Java by using various spatial and remote sensing datasets. The methodology was divided into three stages: first, the Landsat 8 operational land imagery satellite datasets were processed to create layers for the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI) and land surface temperature. For the proposed multicriteria analysis, another seven criteria: distance from roads, distance from rivers, slope, land cover, elevation levels, soil types and rainfall, were considered. Second, a spatial analysis was performed to identify highly suitable areas for maize production using a geographical extent and multicriteria analysis. Third, the criteria were determined using land suitability information for a 5-year period. The land suitability analysis with equal weight showed that 70.8% of the land (136,663 ha) was highly suitable, 26.3% of the land (50,872 ha) was moderately suitable, and 2.8% of the land (5391 ha) was marginally suitable. On the other hand, expert knowledge was also considered using the analytical hierarchy process (AHP) and indicated that 64.9% of the land (125,216 ha) was highly suitable, 30.4% of the land (58,828 ha) was moderately suitable, and 4.5% (8603 ha) of the land was marginally suitable. The yield estimation was determined for the highly suitable areas with NDVI (R2 = 77.81%) and SAVI (R2 = 72.8%). The regression analysis was incorporated to predicted yield of maize. This research recommends that satellite remote sensing, GIS and AHP-based multicriteria analysis can be extended for agricultural extension services to select suitable lands for increasing maize production.

Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009

Abstract: Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.

Revista Brasileira de Engenharia Agrícola e Ambiental

Abstract: A B S T R A C T Soil compaction influences all stages of crop growth, but in many low yield sugarcane fields critical levels and effects of soil compaction are ignored. Therefore, identifying localization and intensity of compaction are very relevant for descompactation of soil. In this context, this study aimed to apply three criteria for delineating zones for soil compaction management: soil layer where first appear values of soil penetration resistance considered critical for sugarcane growth; cone index for 0-40 cm layer, and depth of occurrence of maximum soil penetration resistance. Sampling was carried out in a grid with 113 points spaced at 100 m, georeferenced by means of a Global Positioning System receiver. Soil penetration resistance in eight layers of 5 cm depth, cone index and depth of occurrence of maximum penetration resistance were determined from data gathered by an automatic measuring penetrometer. Estimative of non-sampled values were obtained by means of kriging interpolation, which allowed drawing of contour maps and the definition of four regions in the field for site specific subsoiling . Palavras-chave: Saccharum ssp. resistencia do solo a penetracao geoestatistica penetrometro

Generación de un índice espectro-temporal para la identificación de zonas afectadas por deforestación usando imágenes Landsat.

Abstract: Magister en Geomatica. Linea de Investigacion: Geoinformacion para el uso sostenible de los recursos naturales