Internet of Things-Based Crop Classification Model Using Deep Learning for Indirect Solar Drying

Table 7

The detailed usage of layers in crop classification along with their output shape and param.

Sr. no.	Layers	Output shape	Param
1	Conv2d (Conv2D)	(None, 200, 200, 8)	224
2	Activation (ReLu)	(None, 200, 200, 8)	0
3	Conv2d_1 (Conv2D)	(None, 200, 200, 8)	84
4	Activation_1 (ReLu)	(None, 200, 200, 8)	0
5	Max_pooling2d (MaxPooling2D)	(None, 100, 100, 8)	0
6	Dropout (dropout)	(None, 100, 100, 8)	0
7	Conv2d_2 (Conv2D)	(None, 100, 100, 16)	1168
8	Activation_2 (ReLu)	(None, 100, 100, 16)	0
9	Conv2d_3 (Conv2D)	(None, 100, 100, 16)	2320
10	Activation_3 (ReLu)	(None, 100, 100, 16)	0
11	Max_pooling2d_1 (MaxPooling2D)	(None, 50, 50, 16)	0
12	Dropout_1 (dropout)	(None, 50, 50, 16)	0
13	Flatten (flatten)	(None, 40000)	0
14	Dense (dense)	(None, 5097)	203885097
15	Activation_4 (ReLu)	(None, 5097)	0
16	Dropout_2 (dropout)	(None, 5097)	0
17	Dense_1 (dense)	(None, 23)	117254
18	Activation_5 (SoftMax)	(None, 23)	0