Detecting Crop Disease

Disease detection in crops is of paramount importance. Crop diseases can result in significant yield loss, and disrupt food availability and affordability. Early detection enables timely interventions, prevent spread and minimize loss. We can use machine learning to help detect:

• Fungal Disease: Machine learning can identify diseases such as powdery mildew, rust, leaf spot, anthracnose, downy mildew, and fusarium wilt.

• Bacterial Diseases: ML algorithms can detect bacterial diseases like bacterial blight, bacterial spot, fire blight, and citrus canker.

• Viral Diseases: ML can assist in identifying viral diseases such as mosaic viruses, leaf curl viruses, yellow vein viruses, and tomato spotted wilt virus.

• Nematode Infestation: ML can detect infestation; a common problem in many crops, causing stunted growth, root damage, and yield loss.

• Nutrient Deficiencies: Machine learning algorithms can help identify nutrient deficiencies in crops, including iron deficiency, nitrogen deficiency, phosphorus deficiency, and potassium deficiency.

• Abiotic Stresses: ML techniques can detect crop damage caused by abiotic stresses: drought, heat and cold stress, salinity, and waterlogging.

• Leaf Diseases: Machine learning can aid in detecting various leaf diseases, including leaf blight, leaf spot, leaf rust, and leaf curl.

Between 20% to 40% of global crop production is lost to pests annually. Each year, plant diseases cost the global economy around $220 billion, and invasive insects around $70 billion, according to the Food and Agriculture Organization of the United Nations.