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First agricultural revolution – ~12000BC
Second agricultural revolution – 18th&19th Century
Current situation
• High dependency on fossil fuels
• High dependency on chemicals
• Advanced genetics for increased crop yield
• Fast growing human population
What’s next?
Sustainable Agriculture
Weed control is a fundamental operation for any crop to maintain high yields
90% is performed chemically
Large machinery
• Oil consumption
• CO2 emission
Weeding alternatives
• Mechanical weeding (Hoeing machine) – Not efficient
• Manual weeding – 10 times more expensive
Untargeted application…
• >90% wasted → high costs
• Toxic residues on soil, water, crop → health & environment impact
• Damage on crops → 5-10 % yield losses in average
…of selective herbicides
• New molecules expensive to develop ($350M1)
• Increased legal pressure to ban molecules & reduce quantity
• Development of herbicide-resistant weeds (+10%/year2)
1 Phillips McDougall, 2015
2 “Global Increase in Unique Resitant Cases”, Dr Ian Heap, Weedscience.org, 2016
Autonomous weeding / navigation
• Minimal human intervention
Solar powered
• Sustainable energy & Power autonomy
AI for weed detection
Robotic arms for precision spraying
• Same efficiency up to 40% cheaper
• No chemicals on crops
• Up to 20 times less herbicide
• Allows cheaper & ecological molecules
• Reduces herbicide-resistant weeds problem
Solar panels, 350 W max
Delta robotic arms
Electric motors, 0.5 m/s speed
GPS & IMU for navigation
RGB Camera, 5 MP @10 fps
Jetson TK1 (TX2) for CV & ML
Weed detection @ >1 fps
Row Finder Pipeline
Image DownscalingPlants
Segmentation
Row
Finder
Weed Detect Pipeline
Plants
Segmentation
Feature
ExtractionClassification
Post-processingMap weed positions
to robot coord.
Send weed positions
& sizes to arms
Camera Settings
Adjustment
&
Image Acquisition
Row Finder Pipeline
Image DownscalingPlants
Segmentation
Row
Finder
Weed Detect Pipeline
Plants
Segmentation
Feature
ExtractionClassification
Post-processingMap weed positions
to robot coord.
Send weed positions
& sizes to arms
Camera Settings
Adjustment
&
Image Acquisition
JETSON TK1 JETSON TX2
CPU GPU CPU GPU
424.6 ± 18.1 55.6 ±1.86 130.9 ± 27.9 36.7 ± 10.8
107.2 ± 2.6 0.41 ± 1.21 79.9 ± 15.7 0.21 ± 0.71
208.9 ± 6.1 41.3 ± 8.9 196.7 ± 6.4 14.7 ± 4.92
881.3 ± 20 98.45 ± 9.1 517.3 ± 42.4 51.8 ± 14.1
Plant Pre-Segmentation
Color Space Conversion
Color Normalization
Gaussian Smoothing
Overall
(Mean Computation Times over 90 images in ms)
PROTOTYPE 1 PROTOTYPE 2
HW: JETSON TK1 HW: JETSON TX2
Hand-crafted Features DNN
AdaBoost Classifier DNN
PROTOTYPE 1 PROTOTYPE 2
HW: JETSON TK1 HW: JETSON TX2
Hand-crafted Features DNN
AdaBoost Classifier DNN
OA = 92% F1=0.87
Prec. = 97.2% Rec. = 78.3%
OA = 92.2% F1=0.867
Prec. = 96.3% Rec. = 78.8%
OA = 97.8% F1=0.77
Prec. = 94.4% Rec. = 65.6%
OA = 97.8% F1=0.78
Prec. = 87.3% Rec. = 70.5%
Set 1 – Seen field
(w.r. 33.9%)
Set 2 – Seen field
(w.r. 5.6%)
OA = Overall Binary Accuracy, F1 = Mean F-Score
Prec. = Weed detection precision, Rec. = Weed detection recall
w.r. = Weed / (Weed + Crop) pixels ratio
Effects of Shadow on Color & Visibility
Illumination Changes due to daytime & weather
10am 6pm
Soil & Crop Variation
Field 1 Field 2
PROTOTYPE 1 PROTOTYPE 2
HW: JETSON TK1 HW: JETSON TX2
Hand-crafted Features DNN
AdaBoost Classifier DNN
OA = 79.4% F1=0.531
Prec. = 56.7% Rec. = 49.9%
OA = 92.5% F1=0.863
Prec. = 86.5% Rec. = 86.1%
Mixed Set – Unseen Fields
(w.r. : 23.4%)
OA = Overall Binary Accuracy, F1 = Mean F-Score
Prec. = Weed detection precision, Rec. = Weed detection recall
w.r. = Weed / (Weed + Crop) pixels ratio
JETSON TK1 –
Feat. Extraction + AdaBoost
JETSON TX2 –
Feat. Extraction + Adaboost
Jetson TX2 –
Deep Learning (CNN)
744 ± 503.4 ms 634.5±393.5 ms
987.1 ± 523.7 ms
After Optimization
780 ± 447.6 ms
Full Pipeline
977.4 ± 511.9 ms
Full Pipeline
833.8 ± 397.6 ms
Full Pipeline
879 ± 457.5 ms
(Mean Computation Time over 20 images)
Real-life embedded GPU application that can improve food production
First completely autonomous weeding robot
NVIDIA Jetson TX2 opens new frontiers for embedded platforms
Deep Learning is powerful, but it is no magic wand
Challenging to obtain images covering all situations
