Читать книгу The Digital Agricultural Revolution - Группа авторов - Страница 45

Practicing AI is difficult for the agriculture field. Even for a small field, the condition keeps changing from one area to another. Also, unpredicted weather conditions change soil quality. The presence of pests and diseases often visits the field [3]. Because no two environments are alike, it is difficult to deploy ML and DL-based AI models in the agricultural field although scientists are capable of developing programs for large sectors [35]. Moreover, the testing and validation of such models require more laborious than in other fields. As per Indian agriculture is concerned, the road is not smooth and it is up to the farmers, businessmen, and consumers to use the power of AI to increase production.

While IoT-enabled gadgets and sensors are not prohibitively expensive, buying in quantity can be costly. A proper local network must be set up in addition to the hardware to permit and handle a large amount of data and there comes the issue of data storage, which might be local or cloud-based.

To function, all new technologies necessitate the use of energy. Massive amounts of energy will be required to support a large-scale agricultural activity. Furthermore, many modern robots and solutions continue to operate on fossil fuels, damaging the environment. IoT and other current technologies are not a proper cure for environmental challenges without more sustainable energy or even renewable alternatives.