Multi-Plant and Multi-Crop Leaf Disease Detection and Classification using Deep Neural Networks, Machine Learning, Image Processing with Precision Agriculture - A Review

Abstract

Globally, more than 19,000 fungi are reported to infect agricultural crops with diseases. As the supplier of human energy, crops are seen as being significant. Plant diseases can harm leaves at any point during planting and harvest, greatly reducing crop productivity and the general market’s financial worth. Consequently, the early diagnosis of leaf disease is crucial in farmlands. Agriculture profitability is a key factor in economic growth. This is among the causes why plant disease identification is crucial in the farming sector, as the presence of illness in plants is extremely common. If necessary precautions aren’t followed in these regions, plants suffer major consequences, which impact the grade, volume, or production of the corresponding products. For example, the United States has pine trees that are susceptible to a dangerous illness called small-leaf disease and the backbone of the Indian economy is crop plants. It is advantageous to diagnose plant diseases (Black Spot, other leaf spots, powdery mildew, downy mildew, blight, and canker) using an automated method since it lessens the amount of manpower required to maintain megafarms of crops and does so at an incredibly preliminary phase(when they appear on plant leaves). The computerized identification and classification of plant leaf diseases using an imagery segmented system is presented in this work. It also includes an overview of various disease categorization methods that can be applied to the identification of plant leaf diseases. In order to detect disorders in diverse plant leaves, this study provides a review of diverse plant diseases and several classifying algorithms in deep machine learning.