Final Year Project by Haseeb Ahmad(18i-1579), Muhammad Zargham Masood (18i-0464) and Masood Ahmad (18i-0755) Supervised by Dr. Labiba Fahad Basically PlanteafX is the plant leaf disease detection system which helps in detecting the disease of different plants and crops in early stages. In ipynb file, there is only the code of model that we are going to use to detect the diseases of plants.

Plant diseases have a severe impact on agricultural production, and if plant diseases are not recognized in a timely manner, food insecurity will develop. The principal crops, such as rice, maize, and others, are particularly important for ensuring food security and agricultural productivity. The basis of effective plant disease prevention and control is early warning and forecasting. They perform critical roles in agricultural production management and decision-making. However, in rural parts of developing countries, visual observations of experienced producers are still the predominant method for plant disease identification; this necessitates regular expert monitoring, which can be prohibitively expensive in big farms(Bai et al., 2018). Furthermore, in certain isolated places, farmers may have to travel great distances to reach professionals, making consulting extremely costly and time-consuming. However, this strategy can only be used in a few places and cannot be widely applied(Khirade, 2015). Automatic detection of plant illnesses is an important study topic because it might help monitor enormous fields of crops and, as a result, detect disease symptoms as soon as they occur on plant leaves(Pooja et al., 2017). As a result, finding a quick, automated, less expensive, and accurate technique to detect plant diseases is critical.

Plant disease is a widespread danger to worldwide agricultural output yield and quality, and it accounts for a considerable amount of production expenses. According to reports, plant disease losses contribute for at least a 10% decline in worldwide food supply.(Mutka & Bart, 2015). The detection of plant diseases is one of the most pressing challenges in agriculture. Early disease diagnosis aids in the prevention of disease spread to other plants, resulting in significant economic losses. However, due to the large variety of plants, changes in the course of plant diseases as a result of climate change, and the rapid spread of diseases to new places, even experienced pathologists are unable to detect certain diseases