In recent years, machine vision systems (MVS) with convolutional neural networks (CNN) for precision spraying have been increasingly investigated due to their robust performance in plant detection. However, the high computational requirement of CNNs makes them slow to be adopted in field operations, especially in unstructured working environments such as broadcast-seeded fields. In this study, we developed a modular precision sprayer by distributing the high computational load of CNN among parallel low-cost and low-power vision computing devices. The sprayer utilized a custom precision spraying algorithm based on SSD-MobileNetV1 running on a Jetson Nano 4 GB. The model achieved 76% ${\mathit{mAP}}^{0.5}$ at 19 fps for weed and soybean detection in a broadcast-seeded field. Further, the sprayer targeted all weed samples and exhibited up to 48.89% spray volume reduction with a typical walking speed up to 3.0 km/h, which was three times faster than similar systems with known targeting performance. With these results, the study demonstrated that CNN-based precision spraying in a complex broadcast-seeded field can achieve increased velocity at high accuracy without needing powerful and expensive computational hardware using modular designs.

In recent years, Convolutional Neural Network (CNN) has become an attractive method to recognize and localize plant species in unstructured agricultural environments. However, developed systems suffer from unoptimized combinations of the CNN model, computer hardware, camera configuration, and travel velocity to prevent missed detections. Missed detection occurs if the camera does not capture a plant due to slow inferencing speed or fast travel velocity. Furthermore, modularity was less focused on Machine Vision System (MVS) development. However, having a modular MVS can reduce the effort in development as it will allow scalability and reusability. This study proposes the derived parameter, called overlapping rate ($r_o$), or the ratio of the camera field of view ($S$) and inferencing speed ($fps$) to the travel velocity ($\stackrel{\rightharpoonup }{v}$) to theoretically predict the plant detection rate ($r_d$) of an MVS and aid in developing a CNN-based vision module. Using performance from existing MVS, the values of $r_o$ at different combinations of inferencing speeds (2.4 to 22 fps) and travel velocity (0.1 to 2.5 m/s) at 0.5 m field of view were calculated. The results showed that missed detections occurred when $r_o$ was less than 1. Comparing the theoretical detection rate ($r_{d,th}$) to the simulated detection rate ($r_{d,sim}$) showed that $r_{d,th}$ had a 20% margin of error in predicting plant detection rate at very low travel distances (<1 m), but there was no margin of error when travel distance was sufficient to complete a detection pattern cycle (≥10 m). The simulation results also showed that increasing $S$ or having multiple vision modules reduced missed detection by increasing the allowable ${\stackrel{\rightharpoonup }{v}}_{max}$. This number of needed vision modules was equal to rounding up the inverse of $r_o$. Finally, a vision module that utilized SSD MobileNetV1 with an average effective inferencing speed of 16 fps was simulated, developed, and tested. Results showed that the $r_{d,th}$ and $r_{d,sim}$ had no margin of error in predicting $r_{actual}$ of the vision module at the tested travel velocities (0.1 to 0.3 m/s). Thus, the results of this study showed that $r_o$ can be used to predict $r_d$ and optimize the design of a CNN-based vision-equipped robot for plant detections in agricultural field operations with no margin of error at sufficient travel distance.

Efficient feed management is essential in aquaculture, especially for bottom-feeding species such as shrimp that require feed delivery at the tank bottom. Most commercial automated feeders are designed for surface-feeding fish and are unsuitable for benthic organisms, leading to feed waste and uneven distribution. This study developed and evaluated an automatic bottom feeder capable of dispensing sinking pellets directly to the substrate. The system integrated a 3D-printed auger for precise feed metering and a closed-type centrifugal impeller positioned at the water surface to achieve radial dispersion of feed. An Arduino Uno microcontroller operated the impeller speed (285.98–586.85 rpm), feed mass (95.23–285.68 g), and dispersion time (2–8 s). A Box–Behnken response surface methodology was used to analyze the influence of these parameters on the mean radius spread of feed, supported by image-based uniformity assessment using OpenCV. Results identified impeller speed as the most significant factor (p = 0.010), with optimal dispersion observed at moderate speeds and longer spread durations. The system demonstrated reliable mechanical performance and precise control, providing a novel, programmable solution for uniform feed delivery in shrimp aquaculture and a promising foundation for scalable, automated bottom-feeding technologies.

Saba bananas are widely produced in the Philippines, mainly for food production. The peel of saba was characterized, and some physical properties of it were measured. The heating value, fixed carbon, volatile matter, ash, and moisture content of the peel were also determined. In predicting the elemental composition, the equation of Parikh et al. (2007) was used, yielding an air-to-fuel ratio of 6.92 kgair/ kgbiomass for combustion. A laboratory-scale prototype updraft gasifier was fabricated and tested for efficiency by boiling water on it. The size of the peel affects the ignition time of the syngas and the burning rate of the biomass. Flow rate affects the amount of heat produced and the equivalence ratio during gasification. Thermal efficiencies in different settings are less than 20% due to system heat loss. The highest efficiency was obtained with a low airflow setting, while the lowest efficiency was yielded at a high airflow setting. At an equivalence ratio of 0.208, about 500g of saba peel produced around 1562.63 kJ of heat with a thermal efficiency of 17.46%. For higher efficiency of the peel, it is recommended to design a gasifier that would decrease the system heat loss and process the biomass into briquettes or pellets for a more uniform mass and size distribution.

Deoxyribonucleic acid (DNA)-based biosensors are rapid, cost-effective, and portable devices for monitoring crop pathogens. However, their on-field operations rely on a laboratory-bound heating block, which controls temperature during sample preparation. This study aimed to develop a field-deployable heating block to assist in the DNA hybridization protocol of DNA-based biosensors. It should maintain $95 °\mathrm{C}$, $55 °\mathrm{C}$, and $20 °\mathrm{C}$ for 5, 10, and 5 min, respectively. It had aluminum bars, positive thermal coefficient ceramic heaters, a Peltier thermoelectric module, and DS18B20 thermistors, serving twelve 0.2 mL polymerase chain reaction (PCR) tubes. An Arduino microcontroller employing a proportional–integral–derivative (PID) algorithm with a solid-state relay was utilized. Machine performance for distilled water-filled PCR tubes showed a maximum $10 °\mathrm{C}$ thermal variation. The machine maintained $(96.00\pm 0.97) °\mathrm{C}$, $(55.15\pm 2.17) °\mathrm{C}$, and $(17.75\pm 0.71) °\mathrm{C}$ with root mean square errors (RMSEs) of $1.40 °\mathrm{C}$, $2.18 °\mathrm{C}$, and $2.36 °\mathrm{C}$, respectively. The average thermal rates were $(0.16\pm 0.11) °\mathrm{C}/\mathrm{s}$, $(0.29\pm 0.11) °\mathrm{C}/\mathrm{s}$, and $(0.14\pm 0.07) °\mathrm{C}/\mathrm{s}$ from ambient to $95 °\mathrm{C}$, $95 °\mathrm{C}$ to $55 °\mathrm{C}$, and $55 °\mathrm{C}$ to $20 °\mathrm{C}$, respectively. Overall, the low standard deviations and RMSEs demonstrate thermostable results and robust temperature control.