Accurately obtaining the crop quantity and density is not only crucial for the demand-based input of water and fertilizer in the field but also vital for ensuring the yield and quality of crops. Aerial photography by unmanned aerial vehicles (UAVs) can quickly acquire the distribution image information of crops over a large area. However, the accurate recognition of a single type of dense targets is a huge challenge for most recognition algorithms. Taking banana seedlings as an example in this study, we captured the images of banana plantations by UAVs from high altitudes to explore an efficient recognition method for dense targets. We proposed a strategy of "cut-recognition-stitch" and constructed a counting method based on the improved Faster R-CNN algorithm. First, the images containing highly dense targets were cropped into a large number of image tiles according to different sizes (simulating different flight altitudes), and the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm was adopted to improve the image quality. A banana seedling dataset containing 36 000 image tiles was constructed. Then, the Faster R-CNN network with optimized parameters was used to train the banana seedling recognition model. Finally, the recognition results were reversely stitched together, and a boundary deduplication algorithm was designed to correct the final counting results to reduce the repeated recognition caused by image cropping. The results show that the recognition accuracy of the Faster R-CNN with optimized parameters for banana image datasets of different sizes can reach up to 0.99 at most. The deduplication algorithm can reduce the average counting error for the original aerial images from 1.60% to 0.60%, and the average counting accuracy of banana seedlings reaches 99.4%. The proposed method effectively addresses the challenge of recognizing dense small objects in high-resolution aerial images, providing an efficient and reliable technical solution for intelligent crop density monitoring in precision agriculture.
Musa/growth & development* ; Crops, Agricultural/growth & development* ; Algorithms ; Neural Networks, Computer ; Unmanned Aerial Devices ; Seedlings/growth & development* ; Image Processing, Computer-Assisted/methods* ; Photography ; Agriculture/methods*

Shoot tips of a traditional table banana (Musa spp. cv. Kanthali) of Bangladesh were evaluated for in vitro propagation. Initial surface sterilization (with 0.1% HgCl2 for 12 minutes) of shoot tips was successful but microbial contamination (mostly bacteria) at the rhizomatous base of the explants was observed within 6-15 days after inoculation which eventually killed 85% of inoculated explants. So, for contamination free culture establishment explants were soaked in two broad spectrum antibiotics namely ampicillin and gentamicin. Cent percent contamination free cultures were established by soaking the explants in 400 mg/L ampicillin or 200 mg/L gentamicin for 1h. Antibiotic treated explants were found to be full contamination free but failed to regenerate after 3 weeks of culture. But some of them absorbed media for up to 2nd subculture and showed swelling of explants and some color changes from pale white to light/deep green. Finally, a few days after 3rd subculture, no growth of explants was observed and all treated explants eventually started to die. Among the untreated alive explants the best medium for single shoot development was MS + 4.0 mg/L BA + 0.5 mg/L KT + 15% CW and average time required for shoot development was 18-21 days. But the regeneration percentage was very low (30%). The best medium for shoot multiplication was MS + 4.0 mg/L BA + 2.0 mg/L IAA + 15% CW and only average 3-4 shoots were formed per shoot. Finally, in vitro proliferated shoots produced roots with maximum frequency (90%) in half strength of MS medium fortified with 0.5 mg/L IBA.
Bangladesh ; Culture Techniques ; methods ; Musa ; growth & development ; microbiology ; Plant Stems ; growth & development ; microbiology ; Rhizome ; growth & development ; microbiology ; Sterilization