Browsing by Author "Asad, Muhammad Hamza"

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Open Access
Application of artificial intelligence to variable rate technology in agriculture
(Faculty of Graduate Studies and Research, University of Regina, 2023-12) Asad, Muhammad Hamza; Bais, Abdul; Al-Anbagi, Irfan; Paranjape, Raman; Hamilton, Howard J.; Shirtliffe, Steven
Variable Rate Application (VRA) plays a pivotal role in enhancing agricultural profitability by optimizing the use of resources and promoting consistent crop growth. This also helps mitigate the negative environmental impact of farming practices. However, the implementation of VRA is heavily reliant on data. An effective VRA prescription involves an agronomist’s in-depth knowledge of the soil and crop conditions within Homogenized Management Zones (HMZs). Certain soil attributes like electrical conductivity, elevation, and soil moisture are measured using proximal sensors installed on farm machinery. However, other soil properties like soil texture and Soil Organic Matter (SOM) measurements require soil sampling and laboratorybased testing. Similarly, crop and weed information is gathered via manual scouting. The collected SOM, soil texture, and crop information based on limited sampling may not be representative of whole field conditions resulting in low spatio-temporal resolution of information. Our research seeks to bridge these gaps by proposing costeffective and scalable solutions that improve spatio-temporal resolution. We suggest installing RGB sensors on farm machinery to monitor crop and weed growth, categorize soil texture, and estimate SOM. This high spatio-temporal information gathered is subsequently processed to investigate if improved HMZs can be identified. We develop crop and weed-specific semantic segmentation methods to detect, localize and quantify crops/weeds, yielding a mean Intersection Over Union (mIOU) up to 83%. These semantic segmentation models are customized to handle agricultural image data, minimizing memory usage and computational costs during training and inference. Through this adaptation, we observe a 6% performance improvement in crop and weed semantic segmentation. The efficiency of binary semantic segmentation models is further enhanced by up to 12% using ensemble learning methods. We recognize the strong correlation between soil properties and crop/weed densities and thus use this relationship to our advantage. We train machine learning models to predict crop and weed densities based on soil properties and satellite data. To accurately predict SOM and soil texture from RGB images, we employ a hybrid approach that combines deep learning and conventional image-processing techniques to overcome the challenges posed by uncontrolled field conditions data. Lastly, we explore the potential for identifying HMZs based on resultant high-resolution crop and soil information. Parts of this research are successfully commercialized under the product name ”SWATCAM”.
Open Access
Estimation of Weed Densities for Variable Rate Herbicide Application
(Faculty of Graduate Studies and Research, University of Regina, 2019-09) Asad, Muhammad Hamza; Bais, Abdul; Wang, Zhanle (Gerald); El-Darieby, Mohamed
Use of herbicides is rising globally to maximize crop yield and profitability. Herbicides negatively impact environmental health and biosphere. To lessen its negative effects, herbicides have to be applied judiciously on crops. Precision agriculture practices suggest adoption of site specific weed management techniques by exploiting patchy nature of weed distribution in the fields which requires accurate weed mapping. Despite recent technical advancement and growing awareness about environment protection, site specific weed management has not got traction in farmer community. In this thesis, endeavours are made to develop relatively simple site specific weed control method using weed density based variable rate herbicide application. Soil, Water and Topography (SWAT) maps are being used by farmers for variable rate seeding and fertilizer in prairie lands of Canada. In this work, we investigate relationship between weeds and SWAT zones and present a new method for variable rate herbicide application which combines deep learning and SWAT maps. Average weed densities are estimated in each SWAT zone through deep learning based semantic segmentation in order to help agronomist develop variable rate herbicide prescription. The study simplifies the weed detection system with the objective to enhance savings of herbicide quantities less costs involved in site specific weed control. Manual labeling bottleneck in semantic segmentation is addressed by labeling only weed pixels. Consequently, trained semantic models zeros out crop pixel along with background pixels. The developed model has the advantage to detect new types of weeds. Binary classification of images based on weeds is also studied in this thesis to compare deep learning models. By investigating SWAT zones and weed density relationship, it is found that the zones with higher salinity, organic matter and water content contain higher density of weeds while the driest zones like eroded hill tops have few or no weeds at all. The crop specific semantic segmentation models have shown MIOU values greater than 80% and FWIOU values more than 97%. The trained models also show robustness in detecting unseen weeds. For binary classification problem of detecting weeds in Canola field, VGG19 has shown 100% accuracy compared to other deep learning architectures.