A Survey on Deep Learning Prediction Techniques for Plant Contagion

Abstract

India's economy is heavily reliant on agriculture, which is also a significant source of crop production. The livelihood of a sizable portion of India's population depends on yield production. Agriculture-related problems are a current primary concern in the modern era. The primary challenge for agricultural growth is the need to maintain the wellbeing of the plants and the crops. One industry that significantly affects people's lives and the state of the economy is agriculture. Poor management leads to the loss of agricultural products. The most delicate plant leaves are the first to show symptoms of sickness. The use of equipment to anticipate disease has proven to be quicker, less expensive, and more reliable than farmers' traditional method of manual observation. Most often, disease symptoms are visible on the leaves, stems, and fruits. The crop's productivity is impacted by a number of factors. Climate change, insect infestations, and numerous plant diseases are some of the contributing reasons. An automatic detection system is intended to pick up illness signs as they emerge or progress. In the paper, a method for using deep learning and image processing to detect illnesses in leaves is revealed.