Automatic Detection of Oil Palm Tree from UAV Images Based on the Deep Learning Method

Figures & data

Figure 1. Flowchart of the proposed DL method

Figure 2. High-resolution of a palm tree image used

Figure 3. The training process of faster RCNN

Figure 4. Oil palm tree plantation

Figure 5. The training samples

Table 1. Parameter set of faster RCNN

Parameters	Value
Learning rate	0.001
Momentum	0.9
Gamma	0.1
Weight decay	0.0005
Proposal boxes	300

Figure 6. Detection results on four samples. (a) Crowded palm tree, (b) overlapped with vegetations, (c) sparely distributed palm tree, (d) boundary with few palm trees

Table 2. Detection result of the testing set

Data	TP	FP	FN	Precision	Recall	OA
Testing set	2326	56	60	97.65%	97.49%	97.57%

Table 3. Detection result of site A

Approach	TP	FP	FN	Precision	Recall	OA
ANN	705	386	25	64.62%	96.58%	80.60%
SVM	697	325	33	68.20%	95.48%	81.84%
Proposed method	710	23	20	96.86%	97.26%	97.06%

Table 4. Detection result of site B

Approach	TP	FP	FN	Precision	Recall	OA
ANN	834	365	36	69.56%	95.86%	82.71%
SVM	832	263	38	75.94%	95.63%	85.79%
Proposed method	845	25	25	97.13%	96.02%	96.58%

Table 5. Detection result of site C

Approach	TP	FP	FN	Precision	Recall	OA
ANN	785	182	31	81.18%	96.20%	88.69%
SVM	788	225	28	77.79%	96.57%	87.18%
Proposed method	796	16	20	98.03%	97.55%	97.79%