Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery

Abstract

Unoccupied aerial vehicles (UAVs) have become increasingly commonplace in aiding planning and management decisions in agricultural and horticultural crop production. The ability of UAV-based sensing technologies to provide high spatial (<1 m) and temporal (on-demand) resolution data facilitates monitoring of individual plants over time and can provide essential information about health, yield, and growth in a timely and quantifiable manner. Such applications would be beneficial for cropped banana plants due to their distinctive growth characteristics. Limited studies have employed UAV data for mapping banana crops and to our knowledge only one other investigation features multi-temporal detection of banana crowns. The purpose of this study was to determine the suitability of multiple-date UAV-captured multi-spectral data for the automated detection of individual plants using convolutional neural network (CNN), template matching (TM), and local maximum filter (LMF) methods in a geographic object-based image analysis (GEOBIA) software framework coupled with basic classification refinement. The results indicate that CNN returns the highest plant detection accuracies, with the developed rule set and model providing greater transferability between dates (F-score ranging between 0.93 and 0.85) than TM (0.86–0.74) and LMF (0.86–0.73) approaches. The findings provide a foundation for UAV-based individual banana plant counting and crop monitoring, which may be used for precision agricultural applications to monitor health, estimate yield, and to inform on fertilizer, pesticide, and other input requirements for optimized farm management.

Canopy Volume Extraction of Citrus reticulate Blanco cv. Shatangju Trees Using UAV Image-Based Point Cloud Deep Learning

“…Shatangju tree. With the development of UAV technology, UAV images have also been widely used [49][50][51][52][53][54][55][56]. Studies [10][11][12][13] have shown that UAV tilt photogrammetry can quickly and conveniently obtain images and texture information of forests, pine trees, eucalyptus, etc.…”

Image-Based High-Throughput Phenotyping in Horticultural Crops

“…Unmanned aerial vehicle (UAV) platforms were used for the measurement of various traits in horticultural crops. For example, UAV-based remote sensing coupled with different machine learning approaches was used for disease detection and classification in potato, tomato, banana, pear, and apple [ 16 , 17 , 18 , 19 , 20 , 21 , 22 ], for tree detection in orchards such as banana and citrus [ 23 , 24 , 25 ], for aboveground biomass estimation in onion, potato, tomato, and strawberry [ 26 , 27 , 28 , 29 ], and other traits of fruits and vegetables [ 23 , 30 , 31 ].…”

Is Machine Learning the Best Technique for Plant Detection Through Unmanned Aerial Systems?

“…Image processing, widely used to solve various agriculture problems such as classification, object detection, object counting, and many more, can help extract crucial information from the image automatically. Canopy height models (CHM) derived from structure from motion or light detection and ranging (LiDAR) technologies represent a technique for canopy height, size, and biomass measurement also used to separate crop pixels from soil [11,12]. Likewise, Otsu thresholding, widely used in many computer vision applications and employable with vegetation indexes, is a single-intensity threshold method that separates pixels into the foreground and background classes [13].…”