A fully labelled image dataset of banana leaves deficient in nutrients

Agriculture is the art and science of cultivating the soil, growing crops and raising livestock. Enhancing crop yield becomes essential to improve economy in agriculture sector. Crops need 16 essential nutrients in balanced factor for their proper growth. Deficiency among these essential nutrients causes stunted growth often leading to significant crop loss. Symptoms of nutrient deficiency in plants can be observed visually which needs to be diagnosed correctly to rectify the problem, so that plants can grow healthily, increasing its yield. Among various crops, banana is one of the staple foods for millions of people across country and world. They contain essential nutrients that can have a defending impact on health of the human beings. The year-round availability, affordability, varietal range, taste, nutritive of banana and medicinal value makes it the favorite fruit among all classes of people. In addition, it also has good export potential. Few of the symptoms due to nutrient's deficiency in banana leaves are like curling of leaves, appearance of yellow strips, yellowing of the leaves, bluish color of young leaves. Deficiency symptoms can be visualized prominently on the leaves of the plant. Further, Machine Learning models can be developed to detect nutrient deficiency in leaves and help farmers in taking relevant measures. Thus, a fully labelled dataset becomes essential to train and test these models to detect nutrient deficiency accurately. The dataset created consists of banana leaf images of various categories like Musa acuminata (Dwarf Cavendish), Robusta, Rasthali, Poovan, Monthan, Elakkibale. Images depict deficiency in eight class of nutrients: boron, calcium, iron, potassium, manganese, magnesium, sulphur and zinc. Table 1 summarizes the essential nutrients and their deficiency symptoms visible on the leaves. Dataset also contains images of healthy leaves. Machine Learning Models can be developed by researchers and students and train them by using the created banana dataset to obtain high accuracy.