Onion Crop Monitoring with Multispectral Imagery using Deep Neural Network

Abstract

The world's growing population leads the government of Pakistan to increase the supply of food for the coming years in a well-organized manner. Feasible agriculture plays a vital role for sustain food production and preserves the environment from any unnecessary chemicals by the use of technology for good management. This research presents the design and development of a multi-spectral imaging system for precision agriculture tasks. This imaging system includes an RGB camera and Pi NoIR camera controlled by a raspberry pi in a drone. The images are captured by Unmanned Aerial Vehicle (UAV) and then send images to the Java application. Images are processed to sharp, resize by application. The Normalized Difference Vegetation Index (NDVI) is calculated to determine the crop health status based on real-time data. The Deep Learning (DL) technique is used to recognize the onion crop growth stage using the captured dataset. We express how to implement a progressive model for the deep neural network to recognize the onion crop growth stage. The performance accuracy of the system for batch size 16 is 96.10% and for batch size 32 is 93.80%.

Identifying Growth Patterns in Arid-Zone Onion Crops (Allium Cepa) Using Digital Image Processing

“…The RGB to CIELAB transformation was applied to detect weeds using the nearby neighbors' decision rule and Euclidean distance metric with erosion and dilation. Results were affected by image brightness, with correct results only obtained in certain lighting conditions [18], such as a cloudy day between 9:30 am to 11:00 am during week 16.…”

“…In recent years, there has been increasing interest in using data analysis and machine learning techniques to identify the patterns affecting crop growth and productivity. The authors of [18] presented a Mutual Subspace Method as a classifier in different farm fields and orchards, achieving 75.1% accuracy in high-altitude image-acquisition systems. By analyzing large amounts of data, such as weather patterns, soil quality, and crop yields, farmers and researchers can gain insight into the factors that influence crop growth and use this information to improve agricultural practices.…”