MAIZE PLANT NUTRIENT DEFICIENCY RECOGNITION THROUGH DEEP NEURAL NETWORK - Echalar, Loyd S; Sapin, Sherwin B; Gulay, Crisanto F (Journal of Agriculture and Technology Management; Vol 24 No 3 (2021): Vol 24 No 3 (2021): Vol 24 No 3 (2021): Journal of Agriculture and Technology Management (JATM) Volume 24 ICAMEROB Edition, 2024)
The agricultural sector is one of the most important sectors of our economy. This is one of the reasons why adopting modern technology plays a significant role in the agricultural sector particularly in managing and administering crops for sustainable production. Maize is one of the crops that the Philippines can be proud of. A modern way of producing sustainable maize crops is very essential nowadays, especially to the maize farmers. The development of a mobile application that identifies and classifies the health condition of maize crops as well as the type of diseases and pests that the maize crops have is presented in this paper. The mobile application used image processing technology with image analysis and plant feature extraction for the identification and classification of maize health conditions, diseases, and pests. Local Binary Pattern Histogram (LBPH) and Convolutional Neural Network (CNN) are the algorithms used to provide accurate and reliable findings. Different evaluation and assessment techniques are done to estimate the accuracy of performance and quality of the project. It concludes that the mobile application will greatly support the needs of the agriculture sector and could be extremely beneficial to the maize farmers in terms of addressing difficulties and needs related to maize crop management and administration.