Even though indoor farms have many advantages over conventional farms and more and more nations are extensively adopting indoor farming, the initial setup and the sustained maintenance of these farms is a very costly affair. As they need artificial lighting and temperature maintenance, power consumption is significant. Crops need to be continuously monitored for diseases, which needs inputs from experts. Also, the stages of growth of the plants have to be tracked closely so as to determine the harvest time, which is crucial in planning labour requirements and supply chain. Accomplishing these tasks manually will require a lot of human resources and the human error in detecting diseases and predicting the harvest time goes up with the expanse of the farm.
Deep Learning based Computer Vision models can be engaged to accurately monitor the crops for diseases and also to track the growth of the plants and the maturing of the produce to compute the harvest time, the amount of produce and the manpower required. Early detection of plant diseases can help quarantine the affected plants, preventing a disease outbreak. Apart from this, observing the growth stages of these plants help provide the adequate amount of fertilizers, pesticides, water and other inputs along with optimized light and temperature, assisting in cutting down the wastage of these resources.