ISBN
Formato digital
979-13-87837-54-9
Fecha de publicación
06-10-2025
Licencia
D. R. © Copyright 2025. Alma Y. Alanis, Jorge Galvez, Omar Avalos, Eduardo Méndez-Palos, Jorge D. Rios, Adriana Peña Perez-Negron & Gabriel Martínez Soltero
Todos los contenidos de esta obra se comparten bajo la licencia Creative Commons Atri-bución/Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0). Esto implica que no está autorizado el uso comercial de la obra original ni de las eventuales obras derivadas, las cuales deberán distribuirse bajo la misma licencia que rige la obra original. No obstante, se permite a terceros compartir el contenido siempre y cuando se reconozca debidamente la autoría y la publicación original en esta editorial.
Rebika Rai
Sikkim University
0000-0003-2298-1025
Arunita Das
Universidad de Guadalajara
Krishna Gopal Dhal
Midnapore College (Autonomous)
0000-0002-6748-0569
Buddhadev Sasmal
Midnapore City College
0009-0009-4244-9897
Jorge Gálvez
Universidad de Guadalajara
0000-0001-6595-8605
Acerca de
Mango cultivation plays a pivotal role in the development and growth of a country, contributing significantly to its economy and agricultural sector. The cultivation of mangoes not only provides a valuable source of income for farmers but also boosts export opportunities, fostering economic stability. However, the impact of mango leaf diseases poses a serious threat to global mango production. Diseases like anthracnose, powdery mildew, sooty mould, and gall midge can lead to reduced yields, diminished fruit quality, and economic losses for farmers. The timely detection and management of these diseases are crucial for mitigating their adverse effects. Fortunately, advanced technology, especially deep learning, has shown up as an effective ally in the early detection of these diseases. Therefore, the aim of this study is to present an updated survey report on this topic.
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