Advances in Agricultural and Food Research Journal
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2735-1084

Author(s):  
Noor Ain Hamid ◽  
Nur Farahiah Zakaria ◽  
Nur Aina Lyana Mohamad Ali

Fish farming faces the challenge of the high cost of feeds because of the cost of high-quality protein like fish meal required in food formulations. Therefore, the need for alternative protein sources is much needed. Black soldier larvae (Hermetia illucens) are alternative feed containing high protein. BSF larvae contain high protein levels (42.7% dry matter; DM). Fish diets should contain 32% to 45% protein content. Therefore, it can be a substitute for a fish meal. This study was conducted to investigate the effect of freshwater fish meal replacement with black soldier fly larvae meal (BSFLM) on the growth rate of Clarias gariepenus fingerling.  The effect of freshwater fish meal replacement with black soldier fly larvae (BSFLM) was investigated. This study involved the cultivation of Clarias gariepenus fingerling given BSFLM and a commercial diet. The results showed a difference between the weight gains of C. gariepenus, which were 6.46g in BSFLM, while the commercial diet was 1.9g during 28 days of experiments. There was also  no significant difference (p <.05) in the mean weight gain, specific growth rate (SGR), and survival rate. Using BSFLM as an alternative source of protein in fish farming can reduce costs in the aquaculture industry without changing its quality.


Author(s):  
Danial Mirza Muammar Rozilan ◽  
Marsyita Hanafi ◽  
Roslizah Ali ◽  
Mohd Adib Razak ◽  
Cui Hairu

Automatic plant growth monitoring has received considerable attention in recent years. The demand in this field has created various opportunities, especially for automatic classification using deep learning methods. In this paper, the efficiency of deep learning algorithms in classifying the growth stage of chili plants is studied. Chili is one of the high cash value crops, and automatic identification of chili plant growth stages is essential for crop productivity. Nevertheless, the study on automatic chili plant growth stage classification using deep learning approaches is not widely explored, and this is due to the unavailability of public datasets on the chili plant growth stages. Various deep learning methods, namely Inception V3, ResNet50, and VGG16, were used in the study, and the results have shown that these methods performed well in terms of accuracy and stability when tested on a dataset that consists of 2,320 images of Capsicum annum 'Bird's Eye' plants of various growth stages and imaging conditions. Nevertheless, the results have also shown that the deep learning methods have difficulty classifying images with a complex background where more than one chili plant was captured in an image.


Author(s):  
Seng Teik Ten ◽  
Gaisan Krishnen ◽  
Khairul Asfamawi Khulidin ◽  
Muhd Akhtar Mohamad Tahir ◽  
Mohamad Hafiz Hashim ◽  
...  

Mushroom can be served as flavoursome food, but most importantly, it can be served as nutritional and medicinal value food. Therefore, a mushroom is an essential commodity under the Malaysia National Agro-Food Policy. Currently, mushroom cultivation is being done in the conventional method, not in a proper and hygienic condition. Therefore controlled environment mushroom house (CEMH) has been developed by transforming a greenhouse into a controlled environment mushroom house integrated with the internet of things (IoT) system. This CEMH micro-climate is automatically controlled by the combination of data parameters provided by various types of sensors. The integration of the IoT system has further enhanced this system to overcome extreme weather changes and override the control anytime and anywhere. The computation and monitoring process can be done either locally or remotely. The current system is set up for Grey Oyster mushroom (Pleurotus pulmonaris) cultivation to identify the best isolate to be proposed for mass production. The interconnection of sensors, mechanical and electronic systems is to optimize the growth condition. The developed system manages to control the temperature consistently and relative humidity (% RH) in the range of 18oC to 27oC and % RH not lower than 70%, respectively. For this condition, this system can produce at least 30% more yield than ordinary mushroom houses. Moreover, the contamination rate is successfully kept below 2% and is considered very low compared to ordinary entrepreneur mushroom houses, usually more than 10%. This system can provide the research facility for the high nutritional and medicinal value mushrooms.


Author(s):  
Mohamed Hafeifi Basir ◽  
Intan Nadhirah Masri

Seedling production is a crucial part of the production of fresh vegetables in a plant factory. Light is one of the necessities for plants to produce a healthy seedling before being transplanted to the production area. Different light formulations resulted in different growth performances of the plant. Hence, this study was conducted to aim for suitable light formulation on various types of lettuce in the MARDI Plant Factory. The study was conducted in two stages: 1) seedling production and 2) production area. Treatments were evaluated at the seedlings' production stage using the split-plot experimental design with four replications. LED light treatments (LT) was the main factor with the various ratio of spectrum colour of Red (R), Blue (B), Green (G) and full spectrum. (LT 1; 5R:1B, LT 2; 1R:1B, LT 3; 1R: 2B, LT 4; 2R:1B, LT 5; 4R:1B:1G and LT 6; Full spectrum as control). The sub-factor was lettuce variety (V1; Butterhead, V2; Green Coral, V3; Red Coral and V4; Mini Cos). Variables measured at seedlings production were seed germination. Growth biomass and SPAD value were evaluated in the production area. At seedlings production, the full spectrum lighting shows significant seeds germination percentage compared to other LED lighting, and V1 performed well on germination percentage and time compare to other varieties. The interaction between light treatments and lettuce was observed on the leaf numbers, shoot fresh weight, leaf area, and the shoot-root ratio at the production area. LT 1 and LT 5 on butterhead and green coral significantly affected the number of leaves and leaf area, which were relatively influenced by light quality and ambient temperature. The yield on green coral lettuce grown under LT 1, LT 2, and LT 5 was significantly higher than others. However, plant biomass and SPAD value for all treatments were not significantly different. The allometry of plant was expressed on a shoot-root ratio with LT 2 on green coral shows a significantly higher shoot-root ratio than other treatments. The study's findings showed that light treatment with Red and Blue LED ratio of 5:1, 1:1, and Red, Blue, and Green LED ratio of 4:1:1 light arrangement on the seedling's productions provided optimal growing conditions in the production area butterhead and green coral lettuce in MPF cultivation.


Author(s):  
Maimunah Mohd Ali ◽  
Norhashila Hashim ◽  
Samsuzana Abd Aziz ◽  
Ola Lasekan

A rising awareness for quality inspection of food and agricultural products has generated a growing effort to develop rapid and non-destructive techniques. Quality detection of food and agricultural products has prime importance in various stages of processing due to the laborious processes and the inability of the system to measure the whole of the food production. The detection of food quality has previously depended on various destructive techniques that require sample destruction and a large amount of postharvest losses. Artificial Intelligence (AI) has emerged with big data technologies and high-performance computation to create new opportunities in the multidisciplinary agri-food domain. This review presents the key concepts of AI comprising an expert system, artificial neural network (ANN), and fuzzy logic. A special focus is laid on the strength of AI applications in determining food quality for producing high and optimum yields. It was demonstrated that ANN provides the best result for modelling and effective in real-time monitoring techniques. The future use of AI for assessing quality inspection is promising which could lead to a real-time as well as rapid evaluation of various food and agricultural products.


Author(s):  
Rohazrin Abdul Rani ◽  
Adli Fikri Ahmad Sayuti ◽  
Mohd Khusairy Khadzir ◽  
Muhammad Haniff Ahmad

Fertilisation in grain corn production is an important stage that must be done properly in terms of the amount of fertiliser used to reduce wastage and ensure crop growth. A fertilising implement brand Gasprado, was calibrated and evaluated for its performance to apply urea to grain corn crops at MARDI Seberang Perai, Pulau Pinang. Calibration was conducted to set the right metering for the device's opening to drop  urea that  meets the application rate of 130kg/ha. This was done by measuring the amount of urea dropped for a particular setting and distance. The machine has four metering devices which were labelled as MD1, MD2, MD3 and MD4. Additionally, the fertiliser applicator also comes with spring tine cultivating devices. The implement was tested for fertilising 56 rows of grain corn in the distance of 62 m long. The times taken for the tractor to finish four rows per run along the 62-m distance and to turn at the headland were recorded to evaluate the performance. The implement's metering devices MD1, MD2, MD3 and MD4 were calibrated at setting scales of B-1.5, B-0, B-0 and B-0, respectively that gave the urea application rate of 133 kg/ha, which was the nearest rate to the recommendation. The average working speed of the operation was at 4.08 km/h with the theoretical field capacity to be at 1.224 ha/h. Meanwhile, the machine's effective field capacity was 0.5208 ha/h, which had a field efficiency of 42.5 % for the particular farm design. The use of machine can speed up the operation of applying fertiliser to the grain corn crop but the performance is dependent on the farm layout.


Author(s):  
Mohd shahmihaizan Mat jusoh ◽  
Mohd Nadzim Nordin ◽  
Wan Mohd Aznan Wan Ahamad ◽  
Md Akhir Hamid

Fiber and cocopeat are waste products from coconut husks that can be turned into value added products. Fiber and cocopeat from old coconut husks are well known in coconut industry in the world. This paper described fibre strength from young coconut husks, nutrient content and water-holding ability of young coconut cocopeat compared to old coconut cocopeat. The strength of fiber was determined by using Instron Universal Testing Machine. The results showed that mean load at break for young coconut fiber was 13.76 N while mean load at break for old coconut fiber was 14.93 N. Maximum tensile stress for young coconut fiber was 1.55 MPa and 1.76 MPa for old coconut fiber. The nutrient contents were determined for young cocopeat and old cocopeat resulted as phosphorus (372.79 ppm, 339 ppm), potassium (6829.68 ppm, 10040.46 ppm), calcium (508.74 ppm, 578.40 ppm), magnesium (468.67 ppm, 715.60 ppm) and sodium (1579.70 ppm, 3917.60 ppm). The pH value was 6.55 and 5.39 respectively. The ash contents were 2.62% for young cocopeat while 4.06% for old cocopeat. For water holding ability test, moisture content of each sample from young coconut cocopeat and old coconut cocopeat was determined by using soil moisture meter. After seven days with water added 500 ml two times/day, results showed that water holding ability for peat moss was the best while young cocopeat was better than the old cocopeat. All the results showed that fiber and cocopeat from young coconut husk have high potential for sustainable production in the coconut industry. 


Author(s):  
Mohd Najib Ahmad ◽  
Abdul Rashid Mohamed Shariff ◽  
Ishak Aris ◽  
Izhal Abdul Halin ◽  
Ramle Moslim

The bagworm species of Metisa plana, is one of the major species of leaf-eating insect pest that attack oil palm in Peninsular Malaysia. Without any treatment, this situation may cause 43% yield loss from a moderate attack. In 2020, the economic loss due to bagworm attacks was recorded at around RM 180 million. Based on this scenario, it is necessary to closely monitor the bagworm outbreak at  infested areas. Accuracy and precise data collection is debatable, due to human errors. . Hence, the objective of this study is to design and develop a specific machine vision that incorporates an image processing algorithm according to its functional modes. In this regard, a device, the Automated Bagworm Counter or Oto-BaCTM is the first in the world to be developed with an embedded software that is based on the use of a graphic processing unit computation and a TensorFlow/Teano library setup for the trained dataset. The technology is based on the developed deep learning with Faster Regions with Convolutional Neural Networks technique towards real time object detection. The Oto-BaCTM uses an ordinary camera. By using self-developed deep learning algorithms, a motion-tracking and false colour analysis were applied to detect and count number of living and dead larvae and pupae population per frond, respectively, corresponding to three major groups or sizes classification. Initially, in the first trial, the Oto-BaCTM has resulted in low percentages of detection accuracy for the living and dead G1 larvae (47.0% & 71.7%), G2 larvae (39.1 & 50.0%) and G3 pupae (30.1% & 20.9%). After some improvements on the training dataset, the percentages increased in the next field trial, with amounts of 40.5% and 7.0% for the living and dead G1 larvae, 40.1% and 29.2% for the living and dead G2 larvae and 47.7% and 54.6% for the living and dead pupae. The development of the ground-based device is the pioneer in the oil palm industry, in which it reduces human errors when conducting census while promoting precision agriculture practice.


Author(s):  
Adli Fikri Ahmad Sayuti ◽  
Rohazrin Abdul Rani ◽  
Nurul Ahmad Sayuti

After the pineapple crop is 15 months old, the pineapple will be harvested and pruning process will be done before fertilization work begin. Normally in conventional method, farmers will use a sharp machete or sickle to pruning the leave since pineapple leaves and cob have a high fibers content causing the leaves and cob break easily. The convention method requires a lot of time as well as the need for a large labor force, and the cost of production would also increase in this regard. The objective of the study was to evaluate the performance and effectiveness pruning using a mechanization approach compare to the conventional method in pruning the pineapple crop. In 11th Malaysia plan (RMK-11) a new concept and prototype were developed with 2 blade disc type, powered by gearbox 1:3 ratio and adjustable height for peat soil condition. Using of 38hp of tractor high clearance rubber trek with powered by PTO (Power take off) speed 540 and rpm 1500.The height of the cutter blade can be adjustable according to the height of the crop needed to be pruned. The machine capable working rate is 0.86 ha/hour, speed tractor is 2.03km/hour and the machine efficiency are 92 %. Machined time operation is up to 1.2 hour/ha and operating for 8.4 ha/per-day. As a result, the machine seems have a clean-cut result on pineapple leaves and cob without breaking the pineapple crop.


Author(s):  
Sharifah Hafiza Mohd Ramli ◽  
Yahya Sahari ◽  
Nur Farhana Abdullah ◽  
Siti Rajwani Hashim ◽  
Ahmad Fadhlul Wafiq Abdul Rahman ◽  
...  

Grain corn in nature possesses a tendency to absorb and release moisture even during storage.  Grain respiration will lead to fungal growth, consequently mycotoxin development and decreased nutritional components. Storage in tropical weather like Malaysia, in which the temperature is constantly hot throughout the year (temperature 23–33°C, with relative humidity around 81%) will promote further spoilage to the stored grain corn. Therefore, this paper discussed the properties of grain corn during three months of storage in a Malaysian weather setting. Grain corn with the initial moisture content of 12.5± 0.02% MC bagged in the; a) woven polypropylene jumbo bags, b) woven propylene 40 kg bag and c) plastic sealed container was stored in  two different storage facilities located in MARDI for three months. The grain corn after three months of storage showed a consistent water activity, a darkening value in Chroma index, within the permissible limit of fungal growth and exhibits insect pest development of two major species of Coleoptera family. Grain corn is considered safe after three months of storage because low aflatoxin levels have been found, but the physical structure has been compromised due to insect pest infestations.


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