Overview of Biological Methods of Weed Control

Exotic plants in new ecosystems where they may be of no economic importance and where their original biological enemies may be absent become weeds, difficult to manage by crop farmers. They limit the productivity of the lands and hence affect crop development and yield. Efforts towards reducing reliance on herbicides and other methods for environmental, health, economic and sustainability reasons have led to increasing interest in the biological approach to controlling these weeds. This work therefore presents an overview of the biological approach to weed control with focus on the basic concepts, underlying principles, procedures and current practices, cases and causes of failure and successes. Specifically, this chapter has discussed the underlying principles, general procedures, reasons for relatively slow popularity and adoption of biological weed control, examples of successful biological control of weeds with introduced insects and pathogens, when is weed biological control successful?, things to consider when making the choice of agents to be introduced to control weeds and steps to identifying and introducing biological control agents

An Automated System for Sorting of Freshly Harvested Tomato Fruits

Fruit sorting determines market value. Farmers and traders commonly use physical-eye inspection and handpicking for sorting, but this is labour-intensive and ineffective. This research work aims to develop a sensor-based automated system for sorting freshly harvested tomato fruits. The automated system sorts tomato fruits into small, medium, and big sizes for market value. To evaluate the system performance, 115 fruits were machine-sorted and compared to eye-inspection and physical measurement. Physical measurement was done by measuring the minor, intermediate, and major diameters of each fruit with a Vernier calliper. While the eye-inspection was carried out by manual human examination with the eye. Results show average percentage error between physical measurement and automated sorting is 10.264%, which implies 89.736% accuracy. The influence of conveyor speed at three levels (2.8, 3.4, and 3.9) cm/sec on overall system performance was evaluated, and the optimum speed of 3.4cm/sec was obtained

Financial Deepening, Private Domestic Savings and Per Capita Gdp Growth in Nigeria

A general way of evaluating the economic welfare or living standards of country is through its per capita GDP, This study investigates empirically the impact of financial deepening on per capita GDP growth, the interaction effect of financial deepening and private domestic savings on Nigeria’s GDP per capita growth and how per capita GDP respond to shocks in financial deepening in Nigeria. Financial deepening is represented by, the ratio of private sector credit to gross domestic product (PSC/GDP). This study used quarterly data from 1986 to 2014 and was generated from both the CBN (2015) statistical bulletin and World Bank (2015) database. Concerning the Impact of Financial Deepening on GDP per capita and the Interaction effect of private domestic Savings and Financial deepening on GDP per capita, ARDL model was implore,. This thesis therefore, makes a modest contribution to the literature having identified that  financial deepening can contribute to GDP per capita growth, if there is an improvement in domestic resources mobilization, and efficiency in capital allocation in the country. Simply put, these results appear to reveal that various financial development policies have not contributed enough to Nigeria’s per capita growth. However, if government and financial institutions can encourage mobilization of domestic savings; develop credit and equity markets; minimise financial risk; and ensure efficiency of capital allocation, Nigerians can benefit from the deepening of the financial sector and domestic savings in the long-run development of the country

Evaluation of different packaging types for adoption in safe handling and transportation of fresh tomato fruits in Nigeria

ABSTRACTThis work evaluated plastic, wooden and carton crates and compared the best of them to traditional basket for possible adoption in safe handling and transportation of fresh tomato fruits in Nigeria. This was achieved through laboratory experiments and analysis involving static tests, simulating storage conditions (at average temperature and humidity of 28.920C and 62.08% respectively) and dynamic tests, simulating handling and transportation conditions involving dropping from different heights and vibration at different amplitudes and frequency. Based on experimental results and economic considerations, carton crate was adjudged the best and a new modified design of it was developed. The performance of the designed crate and the traditional basket presently in use in Nigeria was compared. Results of comparative predictive analysis between the carton and the traditional basket revealed that losses that can be incurred using traditional basket while on transit is about 6.25-7.08%; which can be reduced to an average of 5.71% when carton crates are used. In case of accident, the traditional method can incur an average loss of 51.59%, which can be reduced to an average of 37.88% when carton crates are used. In case of delay in travel (2-3weeks), the traditional method may lose an average of 23.81-88.10%, but this can be reduced to 14-67% when carton crates are used

Detecting Cassava Plants under Different Field Conditions Using UAV-Based RGB Images and Deep Learning Models

A significant number of object detection models have been researched for use in plant detection. However, deployment and evaluation of the models for real-time detection as well as for crop counting under varying real field conditions is lacking. In this work, two versions of a state-of-the-art object detection model—YOLOv5n and YOLOv5s—were deployed and evaluated for cassava detection. We compared the performance of the models when trained with different input image resolutions, images of different growth stages, weed interference, and illumination conditions. The models were deployed on an NVIDIA Jetson AGX Orin embedded GPU in order to observe the real-time performance of the models. Results of a use case in a farm field showed that YOLOv5s yielded the best accuracy whereas YOLOv5n had the best inference speed in detecting cassava plants. YOLOv5s allowed for more precise crop counting, compared to the YOLOv5n which mis-detected cassava plants. YOLOv5s performed better under weed interference at the cost of a low speed. The findings of this work may serve to as a reference for making a choice of which model fits an intended real-life plant detection application, taking into consideration the need for a trade-off between of detection speed, detection accuracy, and memory usage