This work reviews the current state of the art for pineapple production in Malaysia from the perspective of mechanization and automation. It examines the issues and challenges facing this industry. The review has led us to the conclusion that pineapple production still relies heavily on manual labour. The problems facing this industry is no different than other food crops in that low yield labour and high cost are the primary issues that need to be tackled. Although numerous engineering research work to overcome production issues has been done for crops such as rice and maize, engineering research for pineapples has been scarce. The lack of engineering research literature on this crop presents an opportunity for the scientific community to invest effort in this relatively untapped industry. This work further proposes areas where the use of Industry 4.0 technologies can be exploited in order to increase productivity and reduce input costs. Cyber-physical systems that could address issues in planting, crop maintenance and harvesting are put forth as a possible solution.
The standard practice among rice farmers in Malaysia is to apply fertilizer using a single application rate for the whole field. However, fertility conditions vary across the field. The excess use of fertilizer leads to increased input cost and can be damaging to the environment. The focus of this research was to develop a method to apply fertilizer on-the-go while sensing the crop nutrient status of rice plants. A machine learning approach was used to develop a crop nitrogen status prediction model. The model used spectral data from an active canopy reflectance sensor and several vegetation indices as inputs. The model was then incorporated into an on-the-go variable rate fertilizer application system. System performance was then evaluated in the field. The results from this work showed that the model had and accuracy of 83% in classifying the nitrogen status of the rice plants. The results also showed that our method was able to save up to 20% fertilizer use while maintaining yield. These findings are important for large estate farmers who are looking to increase productivity and efficiency.
This work is concerned with a new type of realtime reconfigurable control systems that is based on the use of autonomous agents. To this end, two stages have been examined in the context of intelligent agent decision making; the fault detection and identification (FDI) stage and the reconfiguration stage (RC). The FDI stage detects that a fault has occurred. It then further diagnoses the situation. The RC stage follows this by adapting or changing the control architecture to accommodate the fault. The problem is to synchronize or integrate these two stages in the overall structure of a control system on real world applications. The multi-agent architecture proposed in this paper has several advantages in terms of modularity, reliability, ability to learn and achieve overall higher robustness over past "single software" methods. Initial tests on an example system have been carried out to demonstrate this new organisation of reconfigurable control systems.
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