Modelling and Forecasting Fresh Agro-Food Commodity Consumption Per Capita in Malaysia Using Machine Learning

Table 2

Performance assessment of the neural network (, , ) and OLS models in modelling the per capita consumption of 33 selected fresh agro-food items (kg) [3, 47].

Agro-food	MSE
					OLS		OLS
Rice	5.87	6.45	7.34	220.52	227.93	173.98	180.56
Vegetables
Spinach	0.03	0.56	0.76	0.14	0.12	0.18	0.17
Lady’s finger	0.00	0.05	0.04	0.08	0.08	0.16	0.16
Chili	0.05	0.23	1.45	0.08	0.10	0.34	0.39
Long beans	0.02	0.54	1.34	0.01	0.01	0.01	0.02
Cabbage	0.15	0.23	0.28	1.75	1.88	3.47	3.65
Celery cabbage	2.39	4.34	3.45	10.97	11.95	1.00	1.32
Eggplant	0.01	0.34	0.32	0.16	0.18	0.06	0.08
Cucumber	0.47	0.45	1.34	0.57	0.73	0.01	0.05
Tomato	0.08	0.87	0.99	0.36	0.35	0.04	0.04
Livestock
Chicken and duck	3.11	2.67	4.33	12.87	13.56	12.30	12.98
Pork	0.38	0.67	0.45	0.04	0.01	12.82	12.10
Beef and buffalo meat	0.04	0.34	11.77%	0.01	0.01	0.24	0.20
Lamb	0.01	0.23	0.56	0.07	0.07	0.34	0.33
Chicken and duck egg	0.38	0.99	1.45	15.02	15.88	28.14	29.33
Fisheries
Crab	0.00	0.6	1.4	0.00	0.00	0.02	0.02
Mackerel	0.21	0.87	0.34	1.58	1.46	2.37	2.22
Squid	0.09	0.92	1.56	0.19	0.23	0.11	0.14
Tuna	0.19	0.65	0.98	0.61	0.67	0.22	0.25
Prawn	0.01	0.43	0.67	0.00	0.00	0.78	0.77
Fruits
Star fruit	0.01	0.66	1.01	0.04	0.04	0.00	0.00
Papaya	0.89	1.92	2.3	0.15	0.26	0.26	0.39
Jackfruit	0.00	0.89	0.98	0.02	0.02	0.06	0.07
Durian	1.50	1.56	1.78	1.25	1.87	23.23	25.68
Sweet corn	0.09	0.45	0.23	0.31	0.29	0.09	0.08
Guava	0.14	0.23	0.65	0.00	0.00	4.59	4.63
Coconut	0.60	0.45	0.23	4.74	4.03	13.34	14.62
Mango	0.01	0.54	1.32	0.00	0.00	0.02	0.02
Mangosteen	0.00	0.01	0.34	0.01	0.01	0.03	0.03
Pineapple	0.75	1.45	2.34	0.02	0.04	0.21	0.26
Banana	0.31	0.45	0.78	0.14	0.18	8.67	8.98
Rambutan	0.05	0.56	0.34	0.17	0.14	0.77	0.72
Watermelon	0.12	1.45	1.89	3.06	2.92	0.45	0.39
Total	17.95	33.05	44.13	274.93	285.02	288.33	300.62

^∗DOSM [2]; ^#KPASM [48].