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Plant detection is one of the applications of image processing in agriculture that is being linked with various agricultural tasks nowadays - such as the ability to detect nutritional deficiencies in plants based on the appearance of their leaves. This study is an attempt to contribute on the detection of nutrient deficiency through image processing of Brassica Chinensis L., which usually displays symptoms based on NPK nutrient deficiency and is also locally known as pechay. The study is centered on the hardware development that will be mounted on a robotic prototype and be able to assess a pechay’s health and fertilize it if it lacks nutrients. The detection was based on DenseNet121 model which was trained over numerous healthy and deficient pechay images. If the detection part determines that the pechay was healthy, then the fertilizing system will not release fertilizers. But when it determines the plant to be deficient, the fertilizing system will sprinkle fertilizers on the pechay. The overall system was able to achieve an 85% accuracy in an actual farm set-up. The system was further validated by comparing its results versus the visual inspection results of real-life farmer, and it was found out to be still accurate at 86%. The system further assists the farm owners in reducing the expenses of fertilizer usage and any dangers associated with growing a crop of non-deficient pechay plants. Overall, the process of automated fertilizer system was made possible by using nutrient deficiency detection as the decision-making process.

With the increase in urbanization, environmental pollution has increased and has taken a toll on the quality of Lactuca Sactiva L. or lettuce produced in the country. Vertical indoor farming (VIF) is one of the viable solutions in this problem. However, the crops indoor should have enough nutrients and proper lighting as they would receive outdoors to achieve optimal growth. This research aims to develop an algorithm that would detect and classify the quality of Lactuca Sactiva L. in a vertical indoor farm based on its color property. It was done by first collecting the images of the crop every 30 min the whole planting cycle of the lettuce in the VIF. The images that were gathered were classified manually into two categories: healthy and unhealthy and was eventually used as a training data sets. The second phase was the application and validation of the generated model in the VIF for another planting cycle which got an accuracy of 88 %. The final stage was to implement the validated model in another planting cycle which resulted to an accuracy of 96 %. The overall system was then implemented in the three-layer VIF as the trigger to the LED lights. When the system sees “healthy” -the LED is turned off, when it sees “unhealthy” -the LED is turned on and is set to the minimum required hours of exposure. The implementation of this system to the VIF is one of the factors that contributed to the optimal growth of Lactuca Sactiva L. which further resulted in a greener leaves and reduced harvest time.

Indoor farming is a good alternative farming technique especially in urban areas which lacks an arable land and space for growing crops. Since indoor farming uses artificial environment, it faces many difficulties such as getting the right amount of water, light, temperature, and humidity that are required by crops. These factors highly affect the proper growth and development of crops, its quality, and the output yield. There are existing indoor vertical farming facilities that can mimic the natural environment but don’t address the efficiency and cost of the overall system. In this study, an existing indoor vertical farming was modified by designing a low-cost smart monitoring and control system that uses machine learning technique to autonomously control the actuators such as the cooling fans, water valve, and LED lights based on the following descriptors: temperature, humidity, soil moisture level, and the health status of the Lactuca sativa L. or commonly known as lettuce. The modified system achieved a better growth compared to the traditional method. The height, length and width of the lettuce is found to be better by 20 % and the color of the system’s output is greener than the usual. The cost incurred is 54 % and 19 % cheaper with regards to the energy consumption and the water consumption.

One of the factors that affect the quality and quantity of crops is due to stress factors caused by weeds. The presence of weeds in a field of crop is always stressful for the crop plant due to the competition for nutrients, light and moisture. Conventional sprayers have been reported to reduce these negative impacts by simply reducing the herbicide use through selective spraying -targeting only weeds and avoiding the crops. But these systems are inconsiderate to the effects of herbicides’ soil persistence. Soil pH influences the persistence of herbicides especially for triazine. In alkaline soils (higher pH), lesser amount of these herbicides is bound to soil particles, making more available for plant uptake, to which the herbicide persists much longer. As a response to this dilemma, a model that utilizes machine learning and machine vision implemented on an actual sprayer robot was developed to distinguish weeds from crops and considers the soil pH for weed management. The study achieved an accuracy of 77.90 % for weed detection and 64 % for soil pH determination through image processing. The overall precision of the sprayer robot was 89.19 %. The data indicates a promising result as an attempt in considering herbicide persistence through soil pH in weed management. This study further the support and adherence to the principles of conservation agriculture.