|
|
|
|
|
L. G. Divyanth, D. S. Guru, Peeyush Soni, Rajendra Machavaram, Mohammad Nadimi and Jitendra Paliwal
Applications of deep-learning models in machine visions for crop/weed identification have remarkably upgraded the authenticity of precise weed management. However, compelling data are required to obtain the desired result from this highly data-driven ope...
ver más
|
|
|
|
|
|
|
|
|
|
|
Sukij Skawsang, Masahiko Nagai, Nitin K. Tripathi and Peeyush Soni
Integration of ground-based weather variables, satellite-derived host-plant phenology and ANN modelling were applied for rice pest warning and prediction to support an integrated pest management (IPM) programme in the central plain of Thailand.
|
|
|
|
|
|
|
|
|
|
|
Saowalak Kosanlawit, Peeyush Soni, Ganesh P. Shivakoti
Pág. 1 - 17
This research investigates the relationship between equitable water allocation, participation in the local irrigation operation, and improved economic well-being. The study area consisted of the rice-growing districts of Doi Saket and Mae On in Thailand?...
ver más
|
|
|
|
|
|
|
|
|
|
|
Grianggai Samseemoung, Peeyush Soni and Pimsiri Suwan
An image processing-based variable rate chemical sprayer for disease and pest-infested coconut plantations was designed and evaluated. The manual application of chemicals is considered risky and hazardous to workers, and provides low precision. The desig...
ver más
|
|
|
|
|
|
|
|
|
|
|
Grianggai Samseemoung, Peeyush Soni and Chaiyan Sirikul
-
|
|
|
|
|
|