Disease prediction in plants has acquired much attention in recent years.Meteorological factors such as:temperature,relative humidity,rainfall,sunshine play an important role in a plan’s growth only if they are prese...Disease prediction in plants has acquired much attention in recent years.Meteorological factors such as:temperature,relative humidity,rainfall,sunshine play an important role in a plan’s growth only if they are present in adequate amounts as required by the plant.On the other hand,if the factors are inadequate,they may also support the growth of a disease in the plants.The current study focuses on the Rust disease in Aonla fruits and leaves by utilizing a real time dataset of weather parameters.Fifteen different models are tested for spray prediction on conducive days.Two resampling techniques,random over sampling(ROS)and synthetic minority oversampling technique(SMOTE)have been used to balance the dataset and five different classifiers:support vector machine(SVM),logistic regression(LR),k-nearest neighbor(kNN),decision tree(DT)and random forest(RF)have been used to classify a particular day based on weather conditions as conducive or non-conducive.The classifiers are then evaluated based on four performance metrics:accuracy,precision,recall and F1-score.The results indicate that for imbalanced dataset,kNN is appropriate with high precision and recall values.Considering both balanced and imbalanced dataset models,the proposed model SMOTE-RF performs best among all models with 94.6%accuracy and can be used in a real time application for spray prediction.Hence,timely fungicide spray prediction without over spraying will help in better productivity and will prevent the yield loss due to rust disease in Aonla crop.展开更多
In order to maintain the health and vigour and to sustain the productivity, a pruning experiment was conducted on four year old ＂Balwant＂ cultivar of aonla grown in laterite soil, planted at a spacing of 5m×5m....In order to maintain the health and vigour and to sustain the productivity, a pruning experiment was conducted on four year old ＂Balwant＂ cultivar of aonla grown in laterite soil, planted at a spacing of 5m×5m. To find out the best pruning method, six levels of pruning was performed i.e., （1） Light judicious pruning, （2） Detopping of primary branches at 8 feet from ground level ＋ removal of all secondary branches, （3） Detopping of primary branches at 8 feet from the ground level ＋ removal of all secondary branches at 2 feet from the base of primary branches, （4） Detopping of primary branches at 8 feet from the ground level ＋ removal of all secondary branches at 1 feet from the base of primary branches, （5） Light judicious pruning ＋ Detopping of plant canopy of at 8 feet from the ground level, （6） No pruning （control）. Results of three consecutive years of investigation revealed that light judicious pruning of thin, overlapping, criss-crossed, dead, unproductive and looping branches gave highest fruit yield in all the three years and resulted 64.4 percent yield increment over control when average of three years was considered. Severe pruning of primary and secondary branches caused drastic reduction of yield for two consecutive years after pruning. Judicious pruning helped to produce better sizeable and quality fruits.展开更多
文摘Disease prediction in plants has acquired much attention in recent years.Meteorological factors such as:temperature,relative humidity,rainfall,sunshine play an important role in a plan’s growth only if they are present in adequate amounts as required by the plant.On the other hand,if the factors are inadequate,they may also support the growth of a disease in the plants.The current study focuses on the Rust disease in Aonla fruits and leaves by utilizing a real time dataset of weather parameters.Fifteen different models are tested for spray prediction on conducive days.Two resampling techniques,random over sampling(ROS)and synthetic minority oversampling technique(SMOTE)have been used to balance the dataset and five different classifiers:support vector machine(SVM),logistic regression(LR),k-nearest neighbor(kNN),decision tree(DT)and random forest(RF)have been used to classify a particular day based on weather conditions as conducive or non-conducive.The classifiers are then evaluated based on four performance metrics:accuracy,precision,recall and F1-score.The results indicate that for imbalanced dataset,kNN is appropriate with high precision and recall values.Considering both balanced and imbalanced dataset models,the proposed model SMOTE-RF performs best among all models with 94.6%accuracy and can be used in a real time application for spray prediction.Hence,timely fungicide spray prediction without over spraying will help in better productivity and will prevent the yield loss due to rust disease in Aonla crop.
文摘In order to maintain the health and vigour and to sustain the productivity, a pruning experiment was conducted on four year old ＂Balwant＂ cultivar of aonla grown in laterite soil, planted at a spacing of 5m×5m. To find out the best pruning method, six levels of pruning was performed i.e., （1） Light judicious pruning, （2） Detopping of primary branches at 8 feet from ground level ＋ removal of all secondary branches, （3） Detopping of primary branches at 8 feet from the ground level ＋ removal of all secondary branches at 2 feet from the base of primary branches, （4） Detopping of primary branches at 8 feet from the ground level ＋ removal of all secondary branches at 1 feet from the base of primary branches, （5） Light judicious pruning ＋ Detopping of plant canopy of at 8 feet from the ground level, （6） No pruning （control）. Results of three consecutive years of investigation revealed that light judicious pruning of thin, overlapping, criss-crossed, dead, unproductive and looping branches gave highest fruit yield in all the three years and resulted 64.4 percent yield increment over control when average of three years was considered. Severe pruning of primary and secondary branches caused drastic reduction of yield for two consecutive years after pruning. Judicious pruning helped to produce better sizeable and quality fruits.
