Publication

Summary: This book chapter discusses the critical issue of soil erosion. It highlights how soil erosion and land use changes (such as deforestation and urbanization) are interconnected, each contributing to the other. The chapter addresses challenges like climate change, unplanned urbanization, and others. It also explores sustainable land management practices, including conservation agriculture, agroforestry, and the use of GIS and remote sensing for better soil monitoring. The importance of raising awareness and gaining policy support is emphasized for effectively addressing these challenges in the book chapter. 

This chapter critically examines the challenges limiting the transformative potential & large-scale adoption of drone-based technologies in present agriculture, such as high implementation costs,  inadequate technical infrastructure, lack of skilled persons, etc. This work, currently under journal review, explores both the potential and the real-world challenges of using drones in precision agriculture, especially in areas with limited resources. It draws insights from agronomy, remote sensing, and development policy to provide a balanced and evidence-based analysis.

Summary: This study is the first to use UAV-based multispectral and hyperspectral imaging for detecting wheat blast disease, a significant threat to wheat crops, especially in Bangladesh. By analyzing spectral and thermal data from wheat fields, the research aims to develop efficient and accurate methods for detecting varying levels of disease severity. The findings highlight the potential of UAV technology as a promising tool for improving disease monitoring and management strategies, contributing to efforts to safeguard global food security.

Summary:  Biomass prediction for mungbean is crucial because besides being a pulse crop, mungbean is used as a fodder and green manure. This study investigated a) which vegetation indices best predict final biomass and b) how early biomass predictions are attainable. The research was performed on 28 mung bean genotypes and three flights were conducted and best predicted final biomass at 50 DAS. For early estimation at 30 DAS, MSAVI and EVI performed better. The study showed, that remote sensing can estimate mungbean above-ground biomass accumulation and offer reliable data, especially earlier in growth phases. 

Objectives of the Study:  

The present research will be guided by the following objectives-

(1) To assess the performance of seaweed extract in mitigating the effects of drought at the reproductive stage of groundnut,

(2) To evaluate the efficacy of the seaweed extract using drone-based multispectral remote sensing data, and

(3) To develop a yield prediction model using remote sensing, agronomic and physiological parameters.

Methodology:

The experiment was conducted on the Experimental field at Gazipur Agricultural University, Bangladesh using the groundnut variety BARI Chinabadam-8.

The study was organized in a randomized complete block design (RCBD) with four treatments: full irrigation, drought with 4% seaweed solution, drought with 10% seaweed solution, and drought without seaweed solution, each replicated four times. The drought was imposed starting at 45 days after planting and continued until the full pod development stage.

Soil moisture was monitored using a portable soil moisture meter, and seaweed extracts were prepared from Gracilaria spp. 

Agronomic parameters such as plant height, root-to-shoot ratio, fresh and dry yields, and 1000-seed weight were measured throughout the crop cycle. Physiological responses like stomatal conductance, transpiration rate, and leaf vapor pressure deficit were recorded using a LI-COR Porometer/Fluorometer. Remote sensing data was acquired using UAV-based multispectral imaging to monitor vegetation health and drought stress.

Statistical analyses included ANOVA, post hoc comparisons, and regression modeling to identify significant factors affecting groundnut yield and physiological responses.

Findings:

• The research is on-going. 

Broader Implications:

This research demonstrates the potential of seaweed extract as an effective, eco-friendly solution for mitigating drought stress in groundnut crops during the reproductive stage. Its findings could lead to improved agricultural sustainability and enhanced crop resilience in drought-prone regions. 

Objectives of the Study:  

The present research will be guided by the following objectives-

1. To investigate UAV multispectral vegetation indexes that are correlated with nitrogen content. 

2. To evaluate the effect of nitrogen on yield and yield contributing characteristics of cotton.

Methodology:

The study was conducted at the Cotton Research Centre, Gazipur, using a Randomized Complete Block Design (RCBD) on two cotton varieties CB-Hybrid-1 and CB-M-1. Fertilization treatments included five nitrogen levels: T1 (102.97 kg N/ha), T2 (93.61 kg N/ha), T3 (85 kg N/ha), T4 (76.60 kg N/ha), and T5 (68.93 kg N/ha) and tree replications for each. 

Multispectral images were collected at once in a two-week interval throughout the whole growth period, with plant height measured using a Digital Surface Model (DSM). Data records included initial soil status, growth parameters (plant count, height, flowering dates, dry weight, and leaf area), yield metr ics (first fruiting branch node, boll count, single boll weight, 100 seed weight, and seed cotton yield), and fiber quality. SPAD values, leaf moisture, and chlorophyll content were assessed. The multispectral data were preprocessed to create ortho-mosaics and reflectance images using Pix4D. Vegetation Indices were calculated using QGIS. 

Findings:

The analysis is ongoing, with early findings anticipated to enhance understanding of nitrogen's role in cotton cultivation.

Broader Implications:

This research aimed to provide farmers and researchers with site-specific, real-time crop data to enhance precision agriculture. The goal was to enable optimal cotton yield without environmental harm and reduced production costs.

Summary: 

The trend of nitrogen fertilizer overuse in Bangladesh has become a pressing concern, exceeding the global average and necessitating immediate control. As a viable solution, the paper explores how different remote-sensing technologies can optimize nitrogen use. Four major crops—rice, wheat, maize, and cotton—collectively account for nearly half of global nitrogen consumption.

This seminar review focuses on these four major crops and discusses various remote sensing techniques, such as spectral indices, hyperspectral imaging, and multispectral imaging, for effective nitrogen management. Additionally, it evaluates the prediction accuracy of these methods, underscoring their potential for precision nitrogen management in Bangladesh. You can look at my full seminar paper here!