Deep learning techniques to classify agricultural crops through UAV imagery: a review

Abstract

During the last few years, Unmanned Aerial Vehicles (UAVs) technologies are widely used to improve agriculture productivity while reducing drudgery, inspection time, and crop management cost. Moreover, they are able to cover large areas in a matter of a few minutes. Due to the impressive technological advancement, UAV-based remote sensing technologies are increasingly used to collect valuable data that could be used to achieve many precision agriculture applications, including crop/plant classification. In order to process these data accurately, we need powerful tools and algorithms such as Deep Learning approaches. Recently, Convolutional Neural Network (CNN) has emerged as a powerful tool for image processing tasks achieving remarkable results making it the state-of-the-art technique for vision applications. In the present study, we reviewed the recent CNN-based methods applied to the UAV-based remote sensing image analysis for crop/plant classification to help researchers and farmers to decide what algorithms they should use accordingly to their studied crops and the used hardware. Fusing different UAV-based data and deep learning approaches have emerged as a powerful tool to classify different crop types accurately. The readers of the present review could acquire the most challenging issues facing researchers to classify different crop types from UAV imagery and their potential solutions to improve the performance of deep learning-based algorithms.

Overcoming Domain Shift in Neural Networks for Accurate Plant Counting in Aerial Images

“…RGB cameras are the most widely used cameras for crop monitoring [9,10], providing high-resolution images that are useful for identifying plant growth stages, identifying pests and diseases, and estimating crop yield. Thermal cameras, on the other hand, can detect temperature variations in the crop canopy, providing useful information about plant stress [11] and water uptake [12].…”

Smart Farm-Care using a Deep Learning Model on Mobile Phones

“…The increasing population needs to increase food production to maintain high nutritional quality without affecting nature [28][29][30]. Identification of diseases by segmenting the area of interest from a real-world environment are the different operations performed for different agricultural applications [31,[32][33][34]. A system can be developed to perform these operations using various algorithms, like computer vision and image processing.…”

BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

“…Multispectral, RGB, and hyperspectral sensors have been used for crop-disease detection [ 3 ]. Recently, crop-disease detection utilizing a variety of image sensors has shown encouraging results when combining data-driven approaches, such as machine learning (ML) and deep learning (DL) [ 4 ]. Tomatoes are a commercially significant vegetable crop on a global scale, and various pathogens (viral, bacterial, and fungal illnesses [ 5 , 6 , 7 , 8 ]) that affect tomatoes have been identified [ 9 ].…”