Computer Engineering http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/90 2026-08-01T23:36:26Z 2026-08-01T23:36:26Z Nuhu, Kontagora Bello Folorunsho, Abiola Talha Jibril, Bala Abdullahi Abdullahi, Ibrahim Mohammed Daniya, E. Adedigba, A. P http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31892 2026-07-13T23:16:45Z 2024-12-01T00:00:00Z

Title: A COMPUTER VISION-BASED ROBOTIC WEED SPRAYER FOR MAIZE FARMLAND PRECISION FARMING Authors: Nuhu, Kontagora Bello; Folorunsho, Abiola Talha; Jibril, Bala Abdullahi; Abdullahi, Ibrahim Mohammed; Daniya, E.; Adedigba, A. P Abstract: Maize, a staple crop of great worldwide significance, often experiences large production losses due to weed competing with its nutrients. It is important to treat these weeds without any harm on the maize crop. Existing approaches to weed management relied on traditional application of the herbicide which is marred by wastages, and could damage the crops also causing health issues to the consumers. In this research, an advanced robotic weed sprayer that uses deep learning and computer vision to solve the ubiquitous problem of weed control in maize farmland is proposed. The research employed an advanced deep learning algorithm that was trained on a large image dataset of common weed species and maize, allowing for accurate weed identification and focused herbicide application. The system’s real-time image analysis guarantees efficient weed control. The system performs exceptionally well, with 75% precision, 80% recall, 77% F1-score and 85.12% mean Average Precision (mAP) in weed recognition. This highlights its potential to completely transform conventional weed control techniques and represents a significant advancement in precision farming technologies as well as a promising option to improve productivity and sustainability in maize cultivation by minimizing crop damage through precise herbicides usage.

2024-12-01T00:00:00Z Isah, Omeiza Rabiu http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31891 2026-07-13T23:03:45Z 2024-11-03T00:00:00Z

Title: TOWARDS THE DEVELOPMENT OF AN INTELLIGENT EVAPORATIVE COOLING SYSTEM FOR POST-HARVEST STORAGE OF SELECTED FRUITS Authors: Isah, Omeiza Rabiu Abstract: Poor management of post-harvest storage of fruits and vegetables has led to enormous food wastage and economic loss globally. Refrigerating systems have been adopted over the years to avert these losses; however, installing them is expensive and can cause chilling injury and moisture loss to the fruits and vegetables when they go below 20℃ temperature. An evaporative cooling system has recently been widely used to preserve fruits and vegetables because it’s cheap to implement, especially for small-scale farmers. This system reduces the temperature and increases the air humidity in their chamber by removing latent heat from the evaporated water when exposed to sunlight. The existing evaporative system has been efficient in preserving the quality of fruits and vegetables as well as extending their shelf-life; however, they lacked automated operation and control mechanisms, intelligent mechanisms capable of identifying the physical state of the fruits, adaptive control techniques for the storage and remote monitoring, feedback scheme of the system for use by the farmers. The abovementioned limitations have prevented the system from achieving optimal performance in preserving fruits. Hence, this research aims to develop a multi-chamber evaporative cooling preservative system for post-harvest storage of fruits. In the first step, Tomato images were collected and trained with the MobileNetV2 model, achieving accuracy, precision and recall of 88%, 89% and 88% respectively. Overall, the model performs well, however, finetuning the model or using more training data could help improve its performance further.

2024-11-03T00:00:00Z Isah, Omeiza Rabiu Adebayo, S. E Nuhu, Kontagora Bello Umar, Ugbede Buhari Maliki, Danlami http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31890 2026-07-13T23:02:12Z 2024-06-02T00:00:00Z

Title: Development of an Intelligent Evaporative Cooling System for Post-harvest Storage of Tomato Authors: Isah, Omeiza Rabiu; Adebayo, S. E; Nuhu, Kontagora Bello; Umar, Ugbede Buhari; Maliki, Danlami Abstract: This research developed an intelligent evaporative cooling system for post-harvest tomato preservation that adapts to its suitable temperature, humidity, and CO2 states to store, preserve quality, and increase the shelf life of the fruits. This was accomplished through the use of transfer learning for fruit classification, the Internet of Things (IoT) for remote monitoring and shelf life tracking, and the integration of the evaporative cooling system with a CO2 sensor, a temperature sensor, a humidity sensor, an Arduino Uno, and a Raspberry Pi 4b. The system can classify tomato fruit status as ripe or overripe with a prediction accuracy of 87.5% and a receiver operating characteristic (ROC) value of 88.89%. The developed evaporative cooling system extended the shelf life of ripe tomatoes from 5 to 14-17 days at 20℃ and 90% relative humidity and overripe tomatoes from 3 to 9-11 days at 18℃ and 95% relative humidity. These results emphasize the crucial function of evaporative cooling in fruit and vegetable storage, as it extends the shelf life of tomatoes by 180–200%, hence minimizing post-harvest loss as it also increases the farmers’ income, thereby contributing positively to the economy.

2024-06-02T00:00:00Z Isah, Omeiza Rabiu http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31888 2026-07-13T22:47:33Z 2024-06-02T00:00:00Z

Title: Development of an Intelligent Evaporative Cooling System for Post-harvest Storage of Tomato Authors: Isah, Omeiza Rabiu Abstract: This research developed an intelligent evaporative cooling system for post-harvest tomato preservation that adapts to its suitable temperature, humidity, and CO2 states to store, preserve quality, and increase the shelf life of the fruits. This was accomplished through the use of transfer learning for fruit classification, the Internet of Things (IoT) for remote monitoring and shelf life tracking, and the integration of the evaporative cooling system with a CO2 sensor, a temperature sensor, a humidity sensor, an Arduino Uno, and a Raspberry Pi 4b. The system can classify tomato fruit status as ripe or overripe with a prediction accuracy of 87.5% and a receiver operating characteristic (ROC) value of 88.89%. The developed evaporative cooling system extended the shelf life of ripe tomatoes from 5 to 14-17 days at 20℃ and 90% relative humidity and overripe tomatoes from 3 to 9-11 days at 18℃ and 95% relative humidity. These results emphasize the crucial function of evaporative cooling in fruit and vegetable storage, as it extends the shelf life of tomatoes by 180–200%, hence minimizing post-harvest loss as it also increases the farmers’ income, thereby contributing positively to the economy.

2024-06-02T00:00:00Z