Vol. 3, No. 1 (2023)
Table of Contents
UAV-Based NDVI Assessment for Yield Prediction in Triticum aestivum L.
Isabella Grace Brooks
Abstract: Background: Triticum aestivum L. (common wheat) is a main grain that remains significant for the overall food security across the world. Conventional ways of estimating yield involves extensive labor, destructiveness, and limitations in spatial scope. Drones equipped with multiple sen...
Sentinel-2 Satellite Imaging for Nitrogen Management in Oryza sativa L.
Haruto Kenji Kobayashi, Aoi Megumi Nakamura, Ren Takashi Saito
Abstract: Background: Oryza sativa L. (rice) is the world's leading staple cereal crop, feeding over 3.5 billion people. Inefficient nitrogen fertilizer management in rice production accounts for significant environmental losses, economic costs, and contributes to greenhouse gas emissions. ...
GIS-Based Spatial Variability Analysis of Soil Fertility in Zea mays L. Production Systems
Seo Yeon Lee, Ji Hoon Park
Abstract: Background: The soil fertility levels in Zea mays L. are characterized by inherent heterogeneity, while traditional methods of applying fertilizers evenly do not consider the variability of soil properties within the fields, thus leading to under-utilization of fertilizers and unstabl...
Drone-Based Multispectral Imaging for Early Detection of Water Stress in Glycine max (L.) Merr.
Jing Xuan Zhao
Abstract: Background: Glycine max (L.) Merr. (soybean) is one of the most important oilseeds crops worldwide, and it is experiencing unprecedented challenges due to climate change, resulting in drought and water stress. Conventional crop monitoring approaches are not sufficient in terms of thei...
Machine Learning Prediction of Wheat Yield Using Random Forest and Extreme Gradient Boosting Algorithms
Rahul Verma, Amit Singh
Abstract: Background: Wheat (Triticum aestivum L.) is a staple cereal crop serving as a primary food source for over 1.5 billion people globally. Traditional yield prediction methods rely on visual assessment and statistical models with limited accuracy, constraining precision agriculture devel...
Deep Learning-Based Detection of Rice Leaf Blast Caused by Magnaporthe oryzae
Rachel Mei Ling Lim, Nicholas Jun Wei Tan
Abstract: Background: Rice (Oryza sativa L.) is the staple food crop for over 3.5 billion people globally, yet rice leaf blast caused by the fungal pathogen Mangalore Oryza is a devastating disease-causing annual yield loss exceeding 30% in susceptible cultivars. Conventional disease diagnosis ...
Artificial Intelligence-Assisted Weed Classification in Glycine max (L.) Merr. Using Convolutional Neural Networks
Matthias Adrian Baumann
Abstract: Background: The Glycine max (L.) Merr. (soybean) is a significant oilseed crop grown worldwide on about 121 million hectares annually. The competition from weeds reduces the soybean yields by 20–40% and raises the cost of production due to labor-intensive manual identification a...
IoT-Based Smart Irrigation System for Precision Cultivation of Solanum lycopersicum L.
Camille Louise Girard, Louis Antoine Bernard, Élodie Claire Moreau
Abstract: Background: tomato (Solanum lycopersicum L.) is the second most grown horticultural crop in the world as its yearly production exceeds 180 million tons. However, conventional irrigation contributes to 80% of the overall agricultural water consumption thus posing a threat to water shor...
Hyperspectral Imaging for Early Detection of Nutrient Deficiency in Zea mays L.
Vikram Mishra, Anjali Mehta, Sneha Mishra
Abstract: Background: Zea mays L. (maize) is the world's most produced cereal crop, with annual global production exceeding 1.15 billion tonnes. Balanced nutrition is critical for optimal productivity, yet nutrient deficiencies remain a major constraint affecting crop yields in both develop...
Integration of Soil Moisture Sensors and Variable Rate Irrigation Technology for Sustainable Crop Production
Priya Singh, Amit Gupta, Neha Kumar
Abstract: Background: Around 70% of global freshwater is used in agriculture, with irrigation being responsible for 80-90% of water withdrawals in agriculture. Due to climate change, depletion of groundwater, and rapid population growth, irrigation management requires innovative methods. Tradit...