MACHINE LEARNING-BASED DECISION SUPPORT FOR DETECTING MAIZE PLANT DISEASE

MACHINE LEARNING-BASED DECISION SUPPORT FOR DETECTING MAIZE PLANT DISEASE

1.1 Background to the study

Maize is a part of the vital crops in the globe following rice and wheat (IITA, 2005). The crop remains one of the principal grains farmed in the sub-savanna region of Nigeria (Fatima & Adbul, 2005). For several million people in Africa, maize has become one of the main sources of carbohydrates and is a component of animal feed (Romney, Thorne, Lukuyu, & Thornton, 2003). Since it is always one of the first crops gathered for food during a time of need, maize is indeed quickly replacing other crops as the most favoured one. The crop has always received preferential treatment above all other crops, even cassava (Fakorede, 2001). Maize is known to produce considerable quantities of vitamins A and C when consumed in the immature stages. In Nigeria, maize production has gradually increased to industrial levels, with many agro-based enterprises dependent on it as an organic material. As a grain crop, its importance is mainly on the economic value and not on the number of farmers engaged in the cultivation (Iken & Amusa, 2004). 10.2 million tonnes of maize are being produced in the nation, grown on 4.9 million hectares of land, with an average production rate of 2.1 tmm/ha (FAO, 2018). This data places Nigeria behind other African nations like South Africa, which yield greater tons on a less amount of land.

A fundamental difficulty in agricultural economies is the restricted ability to detect infections, resulting in inadequate crop restoration efforts. Visual inspection was mostly employed to pinpoint diseases in plant at the place where they were cultivated, with the diseased plant being contrasted to a healthy one. Plant height, color, leaf form, and leaf density on the branches, as well as alterations in the root system, are all noticed in this example. If there are disease, the appearance of mycelium, sporangiophore, or sclerotia, if any, indicates the presence of a pathogenic fungus.

1.2 Statement of Problem

Plant disease lowers the quantity and efficiency of food, fiber, and biofuel crops as agriculture tries to feed the world's fast expanding population. Losses may be catastrophic or recurring, but they typically account for 42% of the six most important food crops' output. Post-harvest losses can be severe, especially if farms are located far from markets and have inadequate infrastructure and supply chain management. Many infections that are present after harvest also release poisons that can seriously harm consumers' health.

Farmers spend thousands of dollars managing maize disease, sometimes without enough technical support, which has detrimental effects such as inadequate disease control and contamination. In addition, the disease can interfere with the growth and cause damage to cultivated and naturally growing plants

This project focuses at improving maize food security by diminishing crop risk caused by diseases, using machine learning techniques.

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