IIT Bombay Team Builds AI Tool to Spot Drought Risk 4 Weeks Early, Giving Farmers Time to Plan

KrishiDrought is an AI-powered portal developed by researchers at IIT Bombay. It forecasts agricultural drought conditions by estimating how soil moisture may change in the coming weeks. This matters because drought damage can become visible only after important opportunities to respond have passed.

The platform combines satellite observations, climate-related data, dynamical forecasts and deep-learning models. Its map shows where soil moisture may become drier or wetter than normal. Users can examine forecasts for one, two, three or four weeks ahead across states and districts.

Launched on 28 October 2026, KrishiDrought provides weekly forecasts across India. Farmers, government agencies and other stakeholders can use it as an additional decision-making resource. It may support irrigation planning, crop management and broader drought preparedness, while not replacing local agricultural advice.

KrishiDrought looks up to one month ahead, giving users forecasts for one, two, three and four weeks into the future. This advance window is important because agricultural drought can worsen before its effects become obvious in fields. Earlier information can create time for planning instead of relying only on crisis response.

The portal covers India and presents forecasts at the district level. Its approximate spatial resolution is 9 km, so users can examine conditions across broad areas rather than viewing only one farm. The map lets them select individual states and districts.

The forecasts are updated weekly and show whether soil moisture may become drier or wetter than normal. Redder shades indicate drier conditions, while green indicates wetter conditions. These forecasts support decisions by farmers and agencies, but they are intended as an additional resource, not a replacement for local advice.

Four weeks of warning could shift drought planning from reaction to preparation. Farmers might have time to assess a crop’s water needs, arrange available water or rethink an irrigation schedule. That extra time matters because a dry spell can threaten crops before visible damage appears.

For example, if a district’s forecast shows increasingly dry soil, farmers and agricultural authorities could check whether irrigation arrangements need attention. They could also consider crop-management measures and seek local agricultural advice before soil moisture falls further. The map’s colour coding helps users identify areas of concern.

Government agencies could use the information for irrigation planning and drought preparedness. The forecasts do not guarantee what will happen, and they do not replace local advice. However, KrishiDrought gives stakeholders an additional resource for making better-informed decisions before a potential dry spell harms harvests.

KrishiDrought uses several kinds of environmental information because soil moisture depends on more than recent rainfall. Satellite observations track changing conditions across large areas, including variations in soil moisture. Hydroclimatic datasets add information about water, rainfall and climate relationships.

Dynamical forecasts help estimate how environmental conditions may evolve. The platform then feeds these different inputs into advanced AI-based deep-learning models. The models identify patterns across the combined information and generate forecasts of future soil moisture conditions.

The result is a map-based forecast presented at an approximate 9 km resolution and at district level. Users can view conditions one to four weeks ahead. Red shades show drier-than-normal soil moisture, while green shows wetter-than-normal conditions. This combination supports forward-looking drought assessment rather than identifying only conditions that have already developed.

Agricultural drought is a shortage of soil moisture relative to what crops need. It is therefore defined by the water available to plants, not simply by whether rain has fallen. A region may receive some rainfall and still experience agricultural drought if the soil cannot retain enough moisture.

For example, prolonged dry spells can interrupt the water supply between rainfall events. Changing weather patterns may alter conditions, while high water loss from the soil can remove moisture faster than crops can use it. The result is soil that becomes too dry to meet crop requirements.

This distinction explains why KrishiDrought focuses on future soil moisture conditions. Traditional drought assessments have often identified conditions already present. KrishiDrought instead forecasts how moisture may change over the next one to four weeks, giving users more time to consider irrigation, crop management and preparedness.

Soil moisture is important because it is the water available in the ground to meet crops’ requirements. Rainfall alone does not show whether that supply is sufficient. A crop can struggle when the soil remains too dry, even if the surrounding region has received some rain.

Consider a field that gets rainfall but then faces a prolonged dry spell. Changing weather patterns may add uncertainty, while high water loss from the soil can reduce the moisture that remains. If the soil cannot retain enough water, crops may face stress and agricultural drought conditions can develop.

KrishiDrought tracks and forecasts this moisture-related risk rather than focusing only on rain totals. Satellite observations and other environmental data help estimate changing conditions, while deep-learning models forecast future soil moisture. Users can then examine whether conditions may become drier or wetter than normal over four weeks.

Satellite observations give researchers a broad view of changing environmental conditions. The article specifically notes that they help monitor variations in soil moisture across large areas. This is useful for drought assessment because conditions can differ between locations, while inspecting every area directly would not provide the same broad map.

KrishiDrought uses satellite information alongside hydroclimatic datasets and dynamical forecasts. These sources add context about water, rainfall, climate and how environmental conditions may evolve. Deep-learning models bring the inputs together, identify patterns and generate future soil-moisture forecasts.

The platform presents the results at an approximate 9 km spatial resolution and district level. Its map uses red shades for drier-than-normal conditions and green for wetter-than-normal conditions. Users can inspect selected states and districts up to four weeks ahead, supporting earlier planning and preparedness.