Vol. 3, No. 2 (2023)
Table of Contents
UAV-Based Thermal Imaging for Early Detection of Heat Stress in Gossypium hirsutum L.
Rekha Kumar, Manish Verma, Ruchi Yadav
Abstract: Background: Heat stress is one of the crucial constraints affecting Gossypium hirsutum L. (upland cotton) yield since it can negatively impact the boll set, fiber quality, and lint on an increasing number of occasions. Identification of heat stress in the cotton foliage using conventi...
Satellite-Derived Vegetation Indices for Monitoring Crop Nitrogen Status in Pearl Millet
Michael Andrew Johnson, Emily Grace Miller, Daniel Christopher Thompson
Abstract: Background: Pearl millet is suffering from a lack of nitrogen, which makes nitrogen fertilizer a necessity when taking care of this crop. Using leaf tissue analysis or SPAD measurements is difficult and labor-intensive; moreover, they cannot be used in the field. Objective: The study...
GIS-Assisted Land Suitability Analysis for Sustainable Pulse Crop Production
Neha Rani Mishra, Vivek Anand Gupta, Rakesh Chandra Verma
Abstract: Background: Pulse crops are important for global and nutrition security and for effective agricultural systems, but soil heterogeneity, climate variability, and inefficient land allocation hinder their productivity. Objective: This research creates a Geographic Information System (GI...
LiDAR-Based Estimation of Crop Canopy Structure in Precision Agriculture
Kaito Haruki Fujimoto, Mio Ayaka Shimizu, Ren Takashi Kobayashi, Aoi Misaki Kuroda
Abstract: Background: The structure of a crop canopy is an essential metric that demonstrates the health of the plant as well as its biomass production and yields in precision agriculture. Traditional methods of measuring the crop in the field are labor-intensive, have limited spatial reach, an...
Support Vector Machine-Based Prediction of Crop Productivity Using Climatic and Soil Variables
Dr. Rajesh Kumar, Dr. Priya Sharma, Dr. Michael Chen, Dr. Fatima Al-Mansouri
Abstract: Background: Correct forecasting of agricultural output is crucial for food security programs, resource management, and climate-smart farming practices. The traditional methods used in statistical and process-oriented approaches for predicting crop yields fail to reveal the dynamics of...
Deep Neural Network Models for Automated Identification of Nutrient Deficiency Symptoms in Field Crops
Dr. Rajesh Kumar, Dr. Priya Sharma, Dr. Michael Chen, Dr. Fatima Al-Mansouri
Abstract: Background: Nutrient deficiencies in agricultural crops result in significant yield loss around the world. Traditional diagnostic approaches either rely on the observation of agronomists or tissue analysis in laboratories, approaches that are not only slow and subjective, but also are...
Computer Vision-Based Automated Weed Mapping in Conservation Agriculture
Arthur Nicolas Peeters, Laura Elise Dubois, Simon Olivier Janssens
Abstract: Background: Practices employed in conservation agriculture to support soil health like minimum soil disturbance, permanent residue cover, and diversified farming practices promote weed growth thus timely control of weeds is critical for maintaining crop yield without reverting to exte...
Wireless Sensor Networks for Continuous Monitoring of Soil Salinity in Irrigated Agriculture
Lucas Hendrik Vermeer
Abstract: Background: Soil salinization is the most widespread environmental issue across the globe that reduces the yield of crops in farming lands. Traditional methods of measuring soil salinity involve selecting a soil sample and conducting an electrical conductivity measurement in the labor...
Real-Time Canopy Temperature Monitoring Using Infrared Sensors for Crop Stress Assessment
Alessandro Matteo Romano, Giulia Francesca Ricci
Abstract: Background: In the absence of any measures, crop water stress has adverse effects, such as the decrease of yield and water-use efficiency in irrigated agriculture. Canopy temperature is a well-known indicator of stomatal closure and transpiration reduction and infrared thermometry all...
Integration of Environmental Sensors with Cloud Computing for Precision Farming Decision Support
Wei Jun Liu, Yichen Zhang
Abstract: Background: Advancements in precision agriculture are founded in continuous monitoring of environmental conditions. Although traditional agriculture is still a manual process, which causes delays in the operation of systems and difficulties in utilizing resources effectively and produ...